03_linear_models
Peter Flood
2024-09-03
Install Libraries
if (!require("tidybayes")) install.packages("tidybayes")
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C:\Users\pjflood\AppData\Local\Temp\RtmpYf5WcY\downloaded_packagesLoad Libraries
library(openxlsx) #read and write .xlsx
library(RVAideMemoire) #used for testing lm model assumptions*** Package RVAideMemoire v 0.9-83-12 ***library(DHARMa) #checking model assumptions and outputThis is DHARMa 0.4.7. For overview type '?DHARMa'. For recent changes, type news(package = 'DHARMa')library(fitdistrplus) #insight into which distribution to useLoading required package: MASS
Loading required package: survivallibrary(logspline) #k-s test for selected distribution
library(car) #partial regression plotsLoading required package: carDatalibrary(effectsize) #calculate effect sizes
library(ggeffects) #marginal effect plots
library(marginaleffects) #marginal effectsRegistered S3 method overwritten by 'data.table':
method from
print.data.table
Please cite the software developers who make your work possible.
One package: citation("package_name")
All project packages: softbib::softbib()library(emmeans) #posthoc analysesWelcome to emmeans.
Caution: You lose important information if you filter this package's results.
See '? untidy'library(ggpmisc) Loading required package: ggpp
Loading required package: ggplot2
Attaching package: ‘ggpp’
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annotatelibrary(ggpubr) #arranging multipanel plots
Attaching package: ‘ggpubr’
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as_npc, as_npcx, as_npcylibrary(ggrepel) #text labels on plots w/o overlapping
library(rstatix) #used for emmeans wrappers
Attaching package: ‘rstatix’
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cohens_d, eta_squared
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select
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filterlibrary(brms) #Bayesian modelingLoading required package: Rcpp
Loading 'brms' package (version 2.22.0). Useful instructions
can be found by typing help('brms'). A more detailed introduction
to the package is available through vignette('brms_overview').
Attaching package: ‘brms’
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arlibrary(loo) #leave-one-out cross-validation of Bayesian modelsThis is loo version 2.8.0
- Online documentation and vignettes at mc-stan.org/loo
- As of v2.0.0 loo defaults to 1 core but we recommend using as many as possible. Use the 'cores' argument or set options(mc.cores = NUM_CORES) for an entire session.
- Windows 10 users: loo may be very slow if 'mc.cores' is set in your .Rprofile file (see https://github.com/stan-dev/loo/issues/94).library(MuMIn) #multi-model selectionRegistered S3 methods overwritten by 'MuMIn':
method from
nobs.multinom broom
nobs.fitdistr broom
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loolibrary(tidyverse) #data manipulation── Attaching core tidyverse packages ────────────────────────────────────────────────────────── tidyverse 2.0.0 ──
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ℹ Use the ]8;;http://conflicted.r-lib.org/conflicted package]8;; to force all conflicts to become errorslibrary(tidybayes) #data manipulation
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dstudent_t, pstudent_t, qstudent_t, rstudent_tLoad Data
all.grow.merge <- read.xlsx("~/GitHub/EAGER_growth/Outputs/all_grow_lake_join.xlsx") %>%
mutate(age_group = as.factor(age_group))Format data
Mean length per species per age class over the entire dataset
mean.length.age.class <- all.grow.merge %>%
group_by(species, age_group) %>%
summarise(mean = mean(length_mean_mm, na.rm = T)) %>%
mutate(Species = case_when(
species == "black_crappie" ~ "Black Crappie",
species == "bluegill" ~ "Bluegill",
species == "brown_trout" ~ "Brown Trout",
species == "cisco" ~ "Cisco",
species == "common_white_sucker" ~ "White Sucker",
species == "largemouth_bass" ~ "Largemouth Bass",
species == "northern_pike" ~ "Northern Pike",
species == "pumpkinseed_sunfish" ~ "Pumpkinseed Sunfish",
species == "rainbow_trout" ~ "Rainbow Trout",
species == "rock_bass" ~ "Rock Bass",
species == "smallmouth_bass" ~ "Smallmouth Bass",
species == "walleye" ~ "Walleye",
species == "yellow_perch" ~ "Yellow Perch",
T ~ species
)) %>%
rename(Age = age_group) `summarise()` has grouped output by 'species'. You can override using the `.groups` argument.Linear Regressions
Data may not conform well to model assumptions. The goal of these models is to ask whether or not a given species size class is increasing or decreasing in size through time. Not necessary to over interpret the exact amount of change, although we do report that, it should be taken with a grain of salt.
Black Crappie
###Model
#filter data for only Black Crappie, Pomooxis nigromaculatus
pomnig <- all.grow.merge %>% filter(species == "black_crappie") %>%
filter(!age_group %in% c(12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22), #match age groups in BRTs
!is.na(age_group), !is.na(begin_date_year), !is.na(length_mean_mm),
!is.na(log_max_depth), !is.na(logarea)) %>%
mutate(age_group = fct_rev(age_group))
#linear model
pomnig.lm <- lm(length_mean_mm ~ begin_date_year*age_group + log_max_depth + logarea + doy, data = pomnig)
summary(pomnig.lm)
Call:
lm(formula = length_mean_mm ~ begin_date_year * age_group + log_max_depth +
logarea + doy, data = pomnig)
Residuals:
Min 1Q Median 3Q Max
-151.482 -21.158 -1.023 19.055 208.297
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 8.829e+02 8.712e+02 1.013 0.310923
begin_date_year -2.913e-01 4.346e-01 -0.670 0.502785
age_group10 8.873e+02 1.065e+03 0.833 0.404725
age_group9 1.680e+02 9.729e+02 0.173 0.862899
age_group8 -1.341e+02 9.263e+02 -0.145 0.884937
age_group7 -5.381e+02 9.030e+02 -0.596 0.551269
age_group6 -8.386e+02 8.901e+02 -0.942 0.346182
age_group5 -9.864e+02 8.859e+02 -1.113 0.265591
age_group4 -6.794e+02 8.826e+02 -0.770 0.441443
age_group3 -3.673e+02 8.811e+02 -0.417 0.676775
age_group2 -5.982e+01 8.812e+02 -0.068 0.945878
age_group1 1.076e+02 8.923e+02 0.121 0.903982
age_group0 -4.361e+02 1.038e+03 -0.420 0.674366
log_max_depth 3.228e+00 6.873e-01 4.696 2.72e-06 ***
logarea 1.434e+00 3.783e-01 3.789 0.000153 ***
doy 9.739e-02 9.341e-03 10.427 < 2e-16 ***
begin_date_year:age_group10 -4.505e-01 5.315e-01 -0.848 0.396676
begin_date_year:age_group9 -9.674e-02 4.855e-01 -0.199 0.842078
begin_date_year:age_group8 4.880e-02 4.622e-01 0.106 0.915921
begin_date_year:age_group7 2.439e-01 4.506e-01 0.541 0.588273
begin_date_year:age_group6 3.870e-01 4.441e-01 0.871 0.383536
begin_date_year:age_group5 4.520e-01 4.420e-01 1.023 0.306530
begin_date_year:age_group4 2.885e-01 4.403e-01 0.655 0.512390
begin_date_year:age_group3 1.177e-01 4.396e-01 0.268 0.788821
begin_date_year:age_group2 -5.552e-02 4.397e-01 -0.126 0.899519
begin_date_year:age_group1 -1.620e-01 4.453e-01 -0.364 0.715956
begin_date_year:age_group0 9.333e-02 5.195e-01 0.180 0.857425
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 32.64 on 5124 degrees of freedom
(55 observations deleted due to missingness)
Multiple R-squared: 0.7207, Adjusted R-squared: 0.7193
F-statistic: 508.6 on 26 and 5124 DF, p-value: < 2.2e-16#calculate and interpret effect sizes
eta_squared(pomnig.lm) begin_date_year age_group log_max_depth logarea
0.024069335 0.685460028 0.001486910 0.000718171
doy begin_date_year:age_group
0.006187036 0.002806738 #interpret(eta_squared(pomnig.lm), rules = "cohen1992")
#calculate AIC score
AIC(pomnig.lm)[1] 50554.08#examine model fit
testDispersion(pomnig.lm)
DHARMa nonparametric dispersion test via sd of residuals fitted vs. simulated
data: simulationOutput
dispersion = 0.99547, p-value = 0.8
alternative hypothesis: two.sided
simulation.output <- simulateResiduals(fittedModel = pomnig.lm)
residuals(pomnig.lm) 1 2 3 4 5 6 7 8
21.123904853 31.531410044 18.139824291 7.725728562 2.787960356 6.332061000 32.916046075 12.984253604
9 10 11 12 13 14 15 16
10.216477110 23.412068974 17.139313571 36.435239651 -15.577165547 -15.755651075 4.157822559 1.322990337
17 18 19 20 21 22 23 24
3.274090080 5.930857395 -26.342555843 -21.359946138 -0.249704539 23.819921863 36.463336110 31.648785836
25 26 27 28 29 30 31 32
25.193615033 19.912531568 -42.059485593 15.803250935 49.339557703 -0.807154400 24.464013645 -0.896525492
33 34 35 36 37 38 39 40
-15.519374471 -29.613485593 24.058250935 38.756224370 17.819512266 32.572475183 16.171155817 16.883474508
41 42 43 44 45 46 47 48
-44.853485593 -34.658333357 -58.387227265 -82.621742361 -98.769973076 -47.567792495 -83.079650046 -68.190151253
49 50 51 52 53 54 55 56
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57 58 59 60 61 62 63 64
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65 66 67 68 69 70 71 72
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73 74 75 76 77 78 79 80
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81 82 83 84 85 86 87 88
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89 90 91 92 93 94 95 96
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153 154 155 156 157 158 159 160
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161 162 163 164 165 166 167 168
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225 226 227 228 229 230 231 232
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321 322 323 324 325 326 327 328
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329 330 331 332 333 334 335 336
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905 906 907 908 909 910 911 912
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913 914 915 916 917 918 919 920
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921 922 923 924 925 926 927 928
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937 938 939 940 941 942 943 944
11.731611343 40.307740159 19.573759208 18.242200888 28.368362434 27.094794253 20.865513470 20.016324098
945 946 947 948 949 950 951 952
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953 954 955 956 957 958 959 960
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977 978 979 980 981 982 983 984
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985 986 987 988 989 990 991 992
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[ reached 'max' / getOption("max.print") -- omitted 4151 entries ]residuals(pomnig.lm, quantileFunction = qnorm) 1 2 3 4 5 6 7 8
21.123904853 31.531410044 18.139824291 7.725728562 2.787960356 6.332061000 32.916046075 12.984253604
9 10 11 12 13 14 15 16
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17 18 19 20 21 22 23 24
3.274090080 5.930857395 -26.342555843 -21.359946138 -0.249704539 23.819921863 36.463336110 31.648785836
25 26 27 28 29 30 31 32
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33 34 35 36 37 38 39 40
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41 42 43 44 45 46 47 48
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49 50 51 52 53 54 55 56
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57 58 59 60 61 62 63 64
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65 66 67 68 69 70 71 72
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73 74 75 76 77 78 79 80
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81 82 83 84 85 86 87 88
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-36.264584675 -73.070162144 -11.151662831 -7.054584675 -9.246662831 1.980408640 13.642575952 11.249339624
585 586 587 588 589 590 591 592
-14.730849077 34.818980069 12.513687063 20.676718814 1.697210255 6.350768196 7.923774609 31.987020396
593 594 595 596 597 598 599 600
17.584882678 30.253276115 21.687939822 46.567254172 35.595289297 78.583453690 48.228660730 5.103233940
601 602 603 604 605 606 607 608
35.137254172 8.501955964 17.748660730 7.643233940 47.837254172 77.614110939 -33.229393368 -44.951759070
609 610 611 612 613 614 615 616
11.444666811 -0.252621148 -3.725825716 -18.565138817 -7.426078051 -10.801615383 10.487727368 -16.495333189
617 618 619 620 621 622 623 624
-4.627065592 -45.515908429 16.606229641 23.911717950 -1.255333189 13.642302100 -10.428475673 -23.778368858
625 626 627 628 629 630 631 632
35.276105029 -12.085001090 -39.865035524 14.109438362 -29.018334423 17.708297809 -3.267029915 0.495241181
633 634 635 636 637 638 639 640
4.697082456 -29.159568960 -21.966011019 -24.278563204 -13.476895084 -54.624207122 -24.339415689 -45.793004606
641 642 643 644 645 646 647 648
16.662200854 -29.349758819 31.800431040 -1.827439591 4.508103463 9.042200854 -2.583177382 14.904862945
649 650 651 652 653 654 655 656
-16.362581494 -9.669947196 -7.102817827 -29.130608106 -38.309585358 -17.823177382 4.744862945 -15.092581494
657 658 659 660 661 662 663 664
-28.084947196 12.854325031 11.297725227 -28.912273320 37.695206075 10.116822618 -6.685137055 35.427725227
665 666 667 668 669 670 671 672
-42.773431759 26.264809244 17.345167798 25.452200856 56.314513772 -29.650098426 18.644809244 3.375167798
673 674 675 676 677 678 679 680
-9.435931759 -19.455190756 -11.018165536 4.285534190 69.653934471 87.500151710 71.249877509 17.696816178
681 682 683 684 685 686 687 688
-16.317189164 -19.572905285 -6.185678070 19.374287495 -2.498647151 4.273399103 -23.706280073 -12.164571952
689 690 691 692 693 694 695 696
19.374287495 0.463399103 30.521352849 -4.836973562 -3.219297677 14.143975428 11.975312206 16.480851727
697 698 699 700 701 702 703 704
10.951127207 8.412135557 0.394085133 9.337921911 15.536794765 15.849070245 7.361317707 34.039679950
705 706 707 708 709 710 711 712
-22.428203433 -43.124523080 -28.000632718 -35.802408199 -37.651503224 -34.777160473 -29.022410350 9.763847322
713 714 715 716 717 718 719 720
2.457608779 13.319847322 -92.965061789 31.667608779 5.269352136 -2.810464750 9.488619796 21.854129914
721 722 723 724 725 726 727 728
17.915297960 -39.464201279 -6.739157982 19.314129914 -2.404702040 -32.860201279 -6.421657982 -3.545870086
729 730 731 732 733 734 735 736
2.675297960 5.812440132 54.784758822 17.287547954 -8.857149015 0.116044370 9.570157659 13.905460690
737 738 739 740 741 742 743 744
-3.537345925 2.506029706 -9.257930500 -24.462927242 -13.387232637 34.128070395 14.145263780 13.415306077
745 746 747 748 749 750 751 752
51.604679204 0.617915782 53.139907386 -9.635466186 -25.259476081 5.201986657 40.143316390 10.064422482
753 754 755 756 757 758 759 760
-18.888323329 -6.228013343 -5.775439022 26.125388735 -8.613634897 -4.737262364 -7.803711791 65.654866717
761 762 763 764 765 766 767 768
-12.267025192 -5.427319326 -12.134435420 3.630867612 -14.405472488 12.231948325 -11.019458233 -10.984756441
769 770 771 772 773 774 775 776
-4.102601466 -31.073258714 -15.158508592 -8.342051675 -13.113478465 3.517398534 40.046741286 28.021491408
777 778 779 780 781 782 783 784
24.493680729 3.341948325 -33.879458233 -31.304756441 -11.976601466 -36.999925381 -4.998508592 3.093105489
785 786 787 788 789 790 791 792
-19.845843532 -0.102579754 27.804854131 -1.948029303 0.720613902 45.160741020 -12.860843532 -17.644069646
793 794 795 796 797 798 799 800
-7.722579754 13.291970697 -26.195843532 -1.795913088 9.389854131 18.371970697 18.500613902 2.242594221
801 802 803 804 805 806 807 808
7.280701441 10.132524665 -2.590948345 -21.650821227 32.257368107 10.979191332 -35.818396989 -48.117290897
809 810 811 812 813 814 815 816
-74.891805993 -68.702977885 -50.179284698 -37.630713678 -29.980214885 -38.689674686 -30.140130198 -33.076154742
817 818 819 820 821 822 823 824
34.419091403 70.302342950 35.168367774 57.504952655 45.129231818 80.110817571 37.509231818 -7.075275625
825 826 827 828 829 830 831 832
-5.923968856 6.894724375 6.894724375 1.696031144 -8.345275625 43.769904383 17.968325311 -3.621674689
833 834 835 836 837 838 839 840
10.345802606 -43.403568125 -40.852396327 -31.330003994 -8.903353361 -19.795300104 1.547836247 -6.182697435
841 842 843 844 845 846 847 848
-58.361345903 -26.392163753 -50.218984877 -54.692457014 -39.437253470 -16.911014095 2.018646639 8.765588785
849 850 851 852 853 854 855 856
19.327836247 9.057302565 8.432556915 21.849293066 -59.900822189 3.835666431 -10.005690364 -4.935563246
857 858 859 860 861 862 863 864
-7.075338344 -4.503735175 -53.550822189 -6.324333569 -9.107338344 -5.247494792 3.717852202 -21.435690364
865 866 867 868 869 870 871 872
7.764436754 -56.090822189 -31.595690364 -3.759890104 41.293366092 34.094137723 18.756617639 13.586055657
873 874 875 876 877 878 879 880
49.223300255 34.520032758 36.104971056 13.253284306 -9.907329390 -8.018921409 -2.323152124 8.239028458
881 882 883 884 885 886 887 888
-10.974495760 54.766754387 38.621638163 0.219279404 -22.226292995 -16.745186903 7.457390316 7.506289508
889 890 891 892 893 894 895 896
-27.370568044 -5.388491083 -21.928519995 -35.552631294 -33.969071856 -28.229759714 -8.421979133 -35.140337891
897 898 899 900 901 902 903 904
-50.199797692 13.346983508 27.116609725 -9.389890962 -4.717991054 14.616983508 -12.253390275 -3.387812806
905 906 907 908 909 910 911 912
32.312927759 24.492008946 17.156983508 21.186271142 9.227023710 -16.222958804 -2.723699918 46.157868954
913 914 915 916 917 918 919 920
11.000823885 43.987646437 28.303889037 34.453612553 8.337913518 25.933376221 19.822919275 -1.106387447
921 922 923 924 925 926 927 928
-2.668753148 13.233376221 -31.353650001 7.301056993 3.973612553 16.127246852 21.700042888 15.553920728
929 930 931 932 933 934 935 936
37.711400122 -0.178220146 4.758107042 14.220864735 22.067802753 8.175047351 11.635189043 3.895559510
937 938 939 940 941 942 943 944
11.731611343 40.307740159 19.573759208 18.242200888 28.368362434 27.094794253 20.865513470 20.016324098
945 946 947 948 949 950 951 952
24.734655814 19.042317663 12.870822973 -30.988124272 -22.391730135 20.266639335 -6.096830135 -2.048245254
953 954 955 956 957 958 959 960
10.198335621 8.945104028 9.336079053 -20.748990673 -13.670829128 18.075602691 -20.080256149 6.495924991
961 962 963 964 965 966 967 968
12.520033545 16.486756879 23.662999479 26.851957270 38.184701077 -23.441373576 -21.155469305 -29.903237511
969 970 971 972 973 974 975 976
48.049892902 4.002302505 -21.504482096 -9.149416651 -2.474272631 -11.435264912 -10.967674109 -3.712731963
977 978 979 980 981 982 983 984
13.453515499 31.122981817 21.184902833 -7.385407673 19.083673514 14.147536965 3.420274293 -5.273226394
985 986 987 988 989 990 991 992
-12.606148238 -8.462460576 18.147445935 -0.024495355 22.275332729 -6.703879930 -17.463041407 -16.037960493
993 994 995 996 997 998 999 1000
-17.985389555 -23.769824428 -18.155749251 -10.295118752 -34.608813110 -32.488030202 -28.883966151 -12.234193118
[ reached 'max' / getOption("max.print") -- omitted 4151 entries ]plot(pomnig.lm)
###Post-hoc comparisons
#post-hoc comparisons
pomnig.emm <- emmeans(pomnig.lm, ~ begin_date_year*age_group)
pairs(pomnig.emm, simple = "age_group")begin_date_year = 1992:
contrast estimate SE df t.ratio p.value
age_group11 - age_group10 10.1 8.63 5124 1.173 0.9910
age_group11 - age_group9 24.7 8.07 5124 3.060 0.0923
age_group11 - age_group8 36.9 7.73 5124 4.766 0.0001
age_group11 - age_group7 52.2 7.55 5124 6.915 <.0001
age_group11 - age_group6 67.6 7.47 5124 9.058 <.0001
age_group11 - age_group5 86.0 7.43 5124 11.568 <.0001
age_group11 - age_group4 104.8 7.42 5124 14.127 <.0001
age_group11 - age_group3 132.8 7.41 5124 17.917 <.0001
age_group11 - age_group2 170.4 7.42 5124 22.959 <.0001
age_group11 - age_group1 215.1 7.56 5124 28.449 <.0001
age_group11 - age_group0 250.2 8.93 5124 28.024 <.0001
age_group10 - age_group9 14.6 5.66 5124 2.571 0.2963
age_group10 - age_group8 26.7 5.18 5124 5.164 <.0001
age_group10 - age_group7 42.1 4.90 5124 8.587 <.0001
age_group10 - age_group6 57.5 4.77 5124 12.050 <.0001
age_group10 - age_group5 75.9 4.72 5124 16.070 <.0001
age_group10 - age_group4 94.7 4.69 5124 20.170 <.0001
age_group10 - age_group3 122.7 4.68 5124 26.187 <.0001
age_group10 - age_group2 160.3 4.70 5124 34.088 <.0001
age_group10 - age_group1 205.0 4.92 5124 41.680 <.0001
age_group10 - age_group0 240.1 6.83 5124 35.158 <.0001
age_group9 - age_group8 12.2 4.17 5124 2.919 0.1344
age_group9 - age_group7 27.5 3.82 5124 7.204 <.0001
age_group9 - age_group6 43.0 3.66 5124 11.749 <.0001
age_group9 - age_group5 61.3 3.59 5124 17.089 <.0001
age_group9 - age_group4 80.1 3.55 5124 22.560 <.0001
age_group9 - age_group3 108.1 3.54 5124 30.545 <.0001
age_group9 - age_group2 145.7 3.56 5124 40.895 <.0001
age_group9 - age_group1 190.5 3.85 5124 49.489 <.0001
age_group9 - age_group0 225.5 6.10 5124 36.942 <.0001
age_group8 - age_group7 15.4 3.06 5124 5.023 <.0001
age_group8 - age_group6 30.8 2.85 5124 10.810 <.0001
age_group8 - age_group5 49.2 2.76 5124 17.810 <.0001
age_group8 - age_group4 67.9 2.71 5124 25.068 <.0001
age_group8 - age_group3 95.9 2.69 5124 35.603 <.0001
age_group8 - age_group2 133.6 2.73 5124 48.995 <.0001
age_group8 - age_group1 178.3 3.09 5124 57.754 <.0001
age_group8 - age_group0 213.3 5.65 5124 37.733 <.0001
age_group7 - age_group6 15.4 2.31 5124 6.695 <.0001
age_group7 - age_group5 33.8 2.20 5124 15.398 <.0001
age_group7 - age_group4 52.6 2.13 5124 24.665 <.0001
age_group7 - age_group3 80.6 2.11 5124 38.155 <.0001
age_group7 - age_group2 118.2 2.15 5124 54.943 <.0001
age_group7 - age_group1 162.9 2.59 5124 62.797 <.0001
age_group7 - age_group0 198.0 5.40 5124 36.655 <.0001
age_group6 - age_group5 18.4 1.89 5124 9.694 <.0001
age_group6 - age_group4 37.1 1.82 5124 20.412 <.0001
age_group6 - age_group3 65.1 1.80 5124 36.277 <.0001
age_group6 - age_group2 102.8 1.84 5124 55.813 <.0001
age_group6 - age_group1 147.5 2.34 5124 62.975 <.0001
age_group6 - age_group0 182.5 5.28 5124 34.553 <.0001
age_group5 - age_group4 18.8 1.67 5124 11.215 <.0001
age_group5 - age_group3 46.8 1.65 5124 28.394 <.0001
age_group5 - age_group2 84.4 1.70 5124 49.756 <.0001
age_group5 - age_group1 129.1 2.23 5124 57.929 <.0001
age_group5 - age_group0 164.2 5.23 5124 31.376 <.0001
age_group4 - age_group3 28.0 1.56 5124 17.964 <.0001
age_group4 - age_group2 65.6 1.61 5124 40.763 <.0001
age_group4 - age_group1 110.4 2.16 5124 51.051 <.0001
age_group4 - age_group0 145.4 5.20 5124 27.956 <.0001
age_group3 - age_group2 37.6 1.58 5124 23.792 <.0001
age_group3 - age_group1 82.4 2.14 5124 38.515 <.0001
age_group3 - age_group0 117.4 5.19 5124 22.622 <.0001
age_group2 - age_group1 44.7 2.17 5124 20.600 <.0001
age_group2 - age_group0 79.8 5.20 5124 15.339 <.0001
age_group1 - age_group0 35.1 5.37 5124 6.521 <.0001
P value adjustment: tukey method for comparing a family of 12 estimates test(pairs(pomnig.emm, by = "begin_date_year"), by = NULL, adjust = "mvt") contrast begin_date_year estimate SE df t.ratio p.value
age_group11 - age_group10 1992 10.1 8.63 5124 1.173 0.9873
age_group11 - age_group9 1992 24.7 8.07 5124 3.060 0.0730
age_group11 - age_group8 1992 36.9 7.73 5124 4.766 0.0001
age_group11 - age_group7 1992 52.2 7.55 5124 6.915 <.0001
age_group11 - age_group6 1992 67.6 7.47 5124 9.058 <.0001
age_group11 - age_group5 1992 86.0 7.43 5124 11.568 <.0001
age_group11 - age_group4 1992 104.8 7.42 5124 14.127 <.0001
age_group11 - age_group3 1992 132.8 7.41 5124 17.917 <.0001
age_group11 - age_group2 1992 170.4 7.42 5124 22.959 <.0001
age_group11 - age_group1 1992 215.1 7.56 5124 28.449 <.0001
age_group11 - age_group0 1992 250.2 8.93 5124 28.024 <.0001
age_group10 - age_group9 1992 14.6 5.66 5124 2.571 0.2466
age_group10 - age_group8 1992 26.7 5.18 5124 5.164 <.0001
age_group10 - age_group7 1992 42.1 4.90 5124 8.587 <.0001
age_group10 - age_group6 1992 57.5 4.77 5124 12.050 <.0001
age_group10 - age_group5 1992 75.9 4.72 5124 16.070 <.0001
age_group10 - age_group4 1992 94.7 4.69 5124 20.170 <.0001
age_group10 - age_group3 1992 122.7 4.68 5124 26.187 <.0001
age_group10 - age_group2 1992 160.3 4.70 5124 34.088 <.0001
age_group10 - age_group1 1992 205.0 4.92 5124 41.680 <.0001
age_group10 - age_group0 1992 240.1 6.83 5124 35.158 <.0001
age_group9 - age_group8 1992 12.2 4.17 5124 2.919 0.1064
age_group9 - age_group7 1992 27.5 3.82 5124 7.204 <.0001
age_group9 - age_group6 1992 43.0 3.66 5124 11.749 <.0001
age_group9 - age_group5 1992 61.3 3.59 5124 17.089 <.0001
age_group9 - age_group4 1992 80.1 3.55 5124 22.560 <.0001
age_group9 - age_group3 1992 108.1 3.54 5124 30.545 <.0001
age_group9 - age_group2 1992 145.7 3.56 5124 40.895 <.0001
age_group9 - age_group1 1992 190.5 3.85 5124 49.489 <.0001
age_group9 - age_group0 1992 225.5 6.10 5124 36.942 <.0001
age_group8 - age_group7 1992 15.4 3.06 5124 5.023 <.0001
age_group8 - age_group6 1992 30.8 2.85 5124 10.810 <.0001
age_group8 - age_group5 1992 49.2 2.76 5124 17.810 <.0001
age_group8 - age_group4 1992 67.9 2.71 5124 25.068 <.0001
age_group8 - age_group3 1992 95.9 2.69 5124 35.603 <.0001
age_group8 - age_group2 1992 133.6 2.73 5124 48.995 <.0001
age_group8 - age_group1 1992 178.3 3.09 5124 57.754 <.0001
age_group8 - age_group0 1992 213.3 5.65 5124 37.733 <.0001
age_group7 - age_group6 1992 15.4 2.31 5124 6.695 <.0001
age_group7 - age_group5 1992 33.8 2.20 5124 15.398 <.0001
age_group7 - age_group4 1992 52.6 2.13 5124 24.665 <.0001
age_group7 - age_group3 1992 80.6 2.11 5124 38.155 <.0001
age_group7 - age_group2 1992 118.2 2.15 5124 54.943 <.0001
age_group7 - age_group1 1992 162.9 2.59 5124 62.797 <.0001
age_group7 - age_group0 1992 198.0 5.40 5124 36.655 <.0001
age_group6 - age_group5 1992 18.4 1.89 5124 9.694 <.0001
age_group6 - age_group4 1992 37.1 1.82 5124 20.412 <.0001
age_group6 - age_group3 1992 65.1 1.80 5124 36.277 <.0001
age_group6 - age_group2 1992 102.8 1.84 5124 55.813 <.0001
age_group6 - age_group1 1992 147.5 2.34 5124 62.975 <.0001
age_group6 - age_group0 1992 182.5 5.28 5124 34.553 <.0001
age_group5 - age_group4 1992 18.8 1.67 5124 11.215 <.0001
age_group5 - age_group3 1992 46.8 1.65 5124 28.394 <.0001
age_group5 - age_group2 1992 84.4 1.70 5124 49.756 <.0001
age_group5 - age_group1 1992 129.1 2.23 5124 57.929 <.0001
age_group5 - age_group0 1992 164.2 5.23 5124 31.376 <.0001
age_group4 - age_group3 1992 28.0 1.56 5124 17.964 <.0001
age_group4 - age_group2 1992 65.6 1.61 5124 40.763 <.0001
age_group4 - age_group1 1992 110.4 2.16 5124 51.051 <.0001
age_group4 - age_group0 1992 145.4 5.20 5124 27.956 <.0001
age_group3 - age_group2 1992 37.6 1.58 5124 23.792 <.0001
age_group3 - age_group1 1992 82.4 2.14 5124 38.515 <.0001
age_group3 - age_group0 1992 117.4 5.19 5124 22.622 <.0001
age_group2 - age_group1 1992 44.7 2.17 5124 20.600 <.0001
age_group2 - age_group0 1992 79.8 5.20 5124 15.339 <.0001
age_group1 - age_group0 1992 35.1 5.37 5124 6.521 <.0001
P value adjustment: mvt method for 66 tests #export tables
# interpret(eta_squared(pomnig.lm), rules = "cohen1992") %>%
# write.csv(file = "Outputs/Tables/pomnig_eta_squared.csv")
#slope contrasts
# Obtain slope estimates
pomnig.slopes <- emtrends(pomnig.lm, ~age_group, var = "begin_date_year")
# Compare estimated slopes to 0
pomnig.slope.contrasts <- test(pomnig.slopes) %>%
mutate(Species = "Black Crappie") %>%
rename(Age = age_group)
pomnig.slope.contrasts %>%
write.csv(file = "Outputs/Tables/pomnig_emmeans.csv")Plot raw data
(pomnig.length.year.plot <- ggplot(data = pomnig %>%
mutate(Age = as.factor(age_group)),
aes(x = begin_date_year, y = length_mean_mm,
color = Age, fill = Age, group = Age))+
#geom_point()+
geom_smooth(method = "lm")+
stat_poly_eq(use_label("eq"))+
scale_color_viridis_d()+
scale_fill_viridis_d()+
labs(y = "Length (mm)", x = "Year")+
theme_bw()
)`geom_smooth()` using formula = 'y ~ x'
Marginal Effects
result <- predict_response(pomnig.lm, c("begin_date_year", "age_group"))
# plot(result)
# test_predictions(result) %>%
# write.csv(file = "Outputs/Tables/pomnig_pairwise_length_time_slopes.csv", row.names = F)
(pomnig.marginal.plot <- ggpredict(pomnig.lm, terms = c("begin_date_year", "age_group")) %>%
plot()+
#stat_poly_eq(use_label("eq"))+
stat_regline_equation()+
stat_poly_line()+
# annotate(geom = "text", label = "y = 1000 - 0.35x", x = 2000, y = 350)+
# annotate(geom = "text", label = "y = 21 + 0.093x", x = 2000, y = 212)+
# annotate(geom = "text", label = "y = -64 + 0.13x", x = 2000, y = 199)+
# annotate(geom = "text", label = "y = 230 - 0.028x", x = 2000, y = 182)+
# annotate(geom = "text", label = "y = 610 - 0.23x", x = 2000, y = 160)+
# annotate(geom = "text", label = "y = 920 - 0.39x", x = 2000, y = 137)+
# annotate(geom = "text", label = "y = 1200 - 0.55x", x = 2000, y = 110)+
# annotate(geom = "text", label = "y = 1700 - 0.83x", x = 2000, y = 80)+
scale_color_viridis_d()+
scale_fill_viridis_d()+
labs(title = NULL,
y = "Mean Length-at-Age (mm)",
x = "Year")+
guides(fill = guide_legend(title = "Age", reverse = T), color = guide_legend(title = "Age", reverse = T))+
theme_bw()
)Scale for colour is already present.
Adding another scale for colour, which will replace the existing scale.ggsave(filename = "~/GitHub/EAGER_growth/Outputs/Figures/Linear Models/pomnig_marginal_effects_plot.tiff",
pomnig.marginal.plot,
dpi = 300, width = 200, height = 150, units = "mm")
Bluegill
Model
#filter data for only Black Crappie, Pomooxis nigromaculatus
lepmac <- all.grow.merge %>% filter(species == "bluegill") %>%
filter(!age_group %in% c(12, 13, 18), #match age groups in BRTs
!is.na(age_group), !is.na(begin_date_year), !is.na(length_mean_mm),
!is.na(log_max_depth), !is.na(logarea))
#linear model
lepmac.lm <- lm(length_mean_mm ~ begin_date_year*age_group + log_max_depth + logarea + doy, data = lepmac)
summary(lepmac.lm)
Call:
lm(formula = length_mean_mm ~ begin_date_year * age_group + log_max_depth +
logarea + doy, data = lepmac)
Residuals:
Min 1Q Median 3Q Max
-81.369 -17.186 -0.893 15.702 164.623
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 8.224e+02 3.712e+02 2.215 0.026750 *
begin_date_year -4.002e-01 1.870e-01 -2.140 0.032413 *
age_group1 4.992e+02 3.831e+02 1.303 0.192635
age_group2 3.722e+01 3.780e+02 0.098 0.921559
age_group3 -1.834e+02 3.768e+02 -0.487 0.626352
age_group4 -5.010e+02 3.771e+02 -1.328 0.184045
age_group5 -8.804e+02 3.783e+02 -2.327 0.019975 *
age_group6 -1.132e+03 3.801e+02 -2.978 0.002908 **
age_group7 -1.040e+03 3.841e+02 -2.708 0.006776 **
age_group8 -9.559e+02 3.958e+02 -2.415 0.015749 *
age_group9 -7.718e+02 4.208e+02 -1.834 0.066630 .
age_group10 -6.709e+02 4.568e+02 -1.469 0.141955
age_group11 -3.595e+02 5.498e+02 -0.654 0.513266
log_max_depth -1.599e-01 3.676e-01 -0.435 0.663612
logarea 1.030e+00 1.937e-01 5.319 1.06e-07 ***
doy 1.021e-01 4.542e-03 22.478 < 2e-16 ***
begin_date_year:age_group1 -2.400e-01 1.930e-01 -1.244 0.213673
begin_date_year:age_group2 7.473e-03 1.904e-01 0.039 0.968698
begin_date_year:age_group3 1.325e-01 1.898e-01 0.698 0.485283
begin_date_year:age_group4 3.043e-01 1.900e-01 1.601 0.109312
begin_date_year:age_group5 5.051e-01 1.906e-01 2.650 0.008066 **
begin_date_year:age_group6 6.394e-01 1.915e-01 3.339 0.000844 ***
begin_date_year:age_group7 5.995e-01 1.935e-01 3.098 0.001952 **
begin_date_year:age_group8 5.627e-01 1.993e-01 2.823 0.004766 **
begin_date_year:age_group9 4.761e-01 2.117e-01 2.249 0.024560 *
begin_date_year:age_group10 4.299e-01 2.297e-01 1.872 0.061282 .
begin_date_year:age_group11 2.797e-01 2.761e-01 1.013 0.311052
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 24.47 on 11201 degrees of freedom
(124 observations deleted due to missingness)
Multiple R-squared: 0.7607, Adjusted R-squared: 0.7601
F-statistic: 1369 on 26 and 11201 DF, p-value: < 2.2e-16#calculate and interpret effect sizes
eta_squared(lepmac.lm) begin_date_year age_group log_max_depth logarea
4.644139e-04 7.395302e-01 1.945205e-05 5.852724e-04
doy begin_date_year:age_group
1.228110e-02 7.792847e-03 #interpret(eta_squared(lepmac.lm), rules = "cohen1992")
#calculate AIC score
AIC(lepmac.lm)[1] 103698.4#examine model fit
testDispersion(lepmac.lm)
DHARMa nonparametric dispersion test via sd of residuals fitted vs. simulated
data: simulationOutput
dispersion = 0.99795, p-value = 0.904
alternative hypothesis: two.sided
simulation.output <- simulateResiduals(fittedModel = lepmac.lm)
residuals(lepmac.lm) 1 2 3 4 5 6 7 8 9
22.23397044 40.32784460 42.50544812 36.51041643 28.56432731 15.51020391 9.99081360 19.68051353 0.14791951
10 11 12 13 14 15 16 17 18
12.57062876 29.55689469 18.62575046 16.95882559 16.96210164 0.04956499 -8.58640250 -30.64438335 -3.31474103
19 20 21 22 23 24 25 26 27
-8.15455419 -31.64744494 2.51041165 -11.65511533 -4.76545516 2.34849855 -2.34941320 -25.05065716 -3.03152587
28 29 30 31 32 33 34 35 36
-22.19299146 -39.08588181 -54.48091604 21.81171420 6.70882268 -2.94360633 -5.67920069 -2.64018284 -51.37230899
37 38 39 40 41 42 43 44 45
4.06672101 -7.52257931 -7.47867097 -10.67531249 26.61581981 24.48515148 1.93332924 -1.37051198 -4.87160110
46 47 48 49 50 51 52 53 54
7.47427550 -3.12511481 -16.29541488 -5.44175360 25.27194036 16.12804981 -14.01052194 3.83383869 1.19173701
55 56 57 58 59 60 61 62 63
-15.29083031 -17.64075115 25.60778551 27.54978047 25.08381818 3.07404549 5.96255157 -6.91886316 -10.59303052
64 65 66 67 68 69 70 71 72
-11.10932258 -0.21554782 1.59067742 -0.94932258 -21.44958952 -38.12689718 -26.27437901 -49.23581112 6.77051932
73 74 75 76 77 78 79 80 81
-8.69791279 -22.86243839 40.40889947 16.82841764 -21.95327993 26.27370592 9.32147782 -41.46641390 29.87391453
82 83 84 85 86 87 88 89 90
20.15866218 18.21013000 11.74540330 2.04936908 33.94723361 28.37013000 -2.30646752 -5.60102218 -13.98823208
91 92 93 94 95 96 97 98 99
-11.92063092 8.98719473 -15.22855862 -28.23511895 -33.08854949 -30.48471072 -14.19880819 -15.50781310 -13.91821162
100 101 102 103 104 105 106 107 108
-33.74639884 10.90818690 19.51391908 10.90818690 0.52052806 0.24531343 4.30787645 18.28899290 24.46154139
109 110 111 112 113 114 115 116 117
-17.32741694 -34.83870976 -27.05000710 20.78616820 -9.58817825 -11.94668657 -21.20757809 -1.44256527 16.99057759
118 119 120 121 122 123 124 125 126
15.18015908 18.53768138 22.68766651 34.23647839 9.40062018 19.04676806 12.61101471 29.99978772 45.55102384
127 128 129 130 131 132 133 134 135
46.93312105 35.39102384 -32.84743402 31.16013697 -28.81617168 -25.26310620 -32.22634346 -11.17368772 -63.74117168
136 137 138 139 140 141 142 143 144
-78.22856569 30.23882832 19.71606046 -9.90368772 -11.15270084 -21.26146557 -41.31410069 -13.18486830 -9.38265615
145 146 147 148 149 150 151 152 153
8.36441640 12.36366304 10.91130685 4.72006289 10.51317243 -25.88486830 5.43317243 -22.49704268 -31.66784159
154 155 156 157 158 159 160 161 162
-39.01758909 -9.47482065 -21.96414435 27.71674399 42.53510342 14.59255659 24.81167718 40.60491658 -18.37651742
163 164 165 166 167 168 169 170 171
47.56957551 27.80281490 4.30617700 30.64324988 -4.46364678 1.28343082 -1.16730307 13.66884172 4.06484052
172 173 174 175 176 177 178 179 180
19.72088858 18.67997487 8.94249304 31.57406093 4.70953532 19.65755963 23.85510383 26.25610197 28.22693615
181 182 183 184 185 186 187 188 189
8.19177050 21.10554642 40.24960282 35.05160144 36.75017658 -20.71404737 21.07319907 29.93507023 40.92693615
190 191 192 193 194 195 196 197 198
34.54360144 34.21017658 4.38177050 29.07360282 40.80893478 -0.41063293 -6.83587093 -23.10973841 -20.19667639
199 200 201 202 203 204 205 206 207
16.35733700 30.22998926 -2.74929544 15.93400367 22.24053472 34.22923590 16.84896909 59.18598926 36.61207863
208 209 210 211 212 213 214 215 216
20.05844844 2.77139063 -5.58496234 1.89638265 2.78200058 1.56786124 -8.41296810 -15.22749701 4.35889063
217 218 219 220 221 222 223 224 225
-10.02996234 4.36186124 0.22303190 -15.22749701 18.19660918 0.80289063 1.49772997 -2.08295068 -2.29799942
226 227 228 229 230 231 232 233 234
4.99686124 -1.47030144 -11.26821823 1.81889063 5.63186124 -4.85696810 4.22660918 1.92506587 -10.53719745
235 236 237 238 239 240 241 242 243
-2.14462938 21.02159084 22.16348459 19.82235924 -16.98382302 22.37515126 7.63035924 -15.71382302 45.22235924
244 245 246 247 248 249 250 251 252
37.60235924 -11.90382302 -4.01444642 -2.50057128 -1.19876719 -5.20340929 0.51282766 -7.64829045 -1.93562044
253 254 255 256 257 258 259 260 261
-8.28276691 2.19041987 6.38942872 18.77486917 -12.40007596 -7.74217234 -18.14695712 -1.93562044 10.62276206
262 263 264 265 266 267 268 269 270
7.34486917 3.01970955 9.49437956 1.57996407 0.45873209 -8.23582765 -7.37898938 -29.62215068 -32.75293786
271 272 273 274 275 276 277 278 279
-6.58482765 -13.54756081 -5.40748401 -13.06793786 -28.77836840 -10.95866122 -18.27821974 -15.09755427 2.47399460
280 281 282 283 284 285 286 287 288
19.43244452 4.58210255 6.77643967 -2.09206162 1.73689292 -3.67122708 1.70264868 38.84633949 -15.98045785
289 290 291 292 293 294 295 296 297
26.08664868 47.73633949 -16.56554496 -10.35665607 -16.61545785 0.68664868 43.92633949 7.65989242 -4.66557903
298 299 300 301 302 303 304 305 306
1.78965051 4.38725898 11.26936610 2.95442097 1.36631717 -25.45895095 -20.27444581 -12.19087309 -23.29043569
307 308 309 310 311 312 313 314 315
5.78964204 5.18554733 -10.35916432 1.13052649 -17.03905035 -25.34917570 -17.23080734 -18.38739284 -20.30850840
316 317 318 319 320 321 322 323 324
-36.18117246 -42.12120739 -22.27266961 -20.63345466 -20.10226598 -11.59495595 -11.08389447 -19.32883879 -9.45567387
325 326 327 328 329 330 331 332 333
-26.29766444 -6.34001322 -1.80819905 13.46876039 10.66574257 13.34363896 -3.28108774 6.66064388 31.62074257
334 335 336 337 338 339 340 341 342
26.04363896 23.38891226 -6.03935612 -9.78766444 -23.16751322 11.78988613 -3.05618669 -18.85819005 -23.76497259
343 344 345 346 347 348 349 350 351
-14.92571714 -12.29343306 1.15228766 0.50522214 8.51739505 2.81756331 9.38939295 -24.59549145 12.89161586
352 353 354 355 356 357 358 359 360
-2.46394039 4.40284188 7.81161586 28.45512541 14.30618918 25.16007349 2.80817738 18.58450855 22.48717141
361 362 363 364 365 366 367 368 369
42.84845874 14.47552251 -1.50992651 -11.19582274 -19.60362856 -23.50358327 -37.06112402 1.39698623 -8.67069019
370 371 372 373 374 375 376 377 378
-30.70024994 -25.98668006 -25.88223962 -14.61969348 -18.77312402 17.38430652 6.11887598 17.38430652 -11.70606046
379 380 381 382 383 384 385 386 387
8.53043691 8.94965513 7.66204333 3.32411362 -2.31817115 -16.58683448 -16.58398094 -10.37845198 39.20161362
388 389 390 391 392 393 394 395 396
6.27316552 -13.47519638 -8.25891760 -15.45845198 3.73316552 -6.42398094 28.06510404 24.57364109 19.27549780
397 398 399 400 401 402 403 404 405
25.24847486 14.09070700 13.30108085 5.13800917 7.04300917 20.46175547 -9.54148383 -4.60888959 -25.76544778
406 407 408 409 410 411 412 413 414
-52.43587679 8.32713984 -6.59019496 -11.82522919 -9.91879915 -13.52978328 -10.50457537 -23.68081933 -12.37697632
415 416 417 418 419 420 421 422 423
-2.32844190 -36.40866559 -22.53697632 -40.85177523 4.17149128 -7.73175495 -14.13631508 -20.57603603 -19.54620111
424 425 426 427 428 429 430 431 432
-25.78804075 -32.43795810 -9.79850872 -25.72342161 -18.27620111 -7.28232646 -19.31462477 -23.54795810 37.46233711
433 434 435 436 437 438 439 440 441
23.21331923 31.97033285 40.87690846 31.40863806 27.12772851 19.46485077 2.20271677 15.43826328 28.67495431
442 443 444 445 446 447 448 449 450
31.03537323 21.69075731 17.06531312 8.51895144 -5.13542265 -9.01561657 9.56451328 14.01731312 -5.20561657
451 452 453 454 455 456 457 458 459
-33.57931355 -46.99979538 -9.25344670 -16.74078123 -25.11467256 -22.78431355 -31.33646204 -23.44459112 -25.19775309
460 461 462 463 464 465 466 467 468
-33.74472878 -38.94866730 -44.65361162 -28.49931355 -44.45979538 3.51272828 9.68682662 5.31668676 -7.41643135
469 470 471 472 473 474 475 476 477
-14.21201387 -29.83090462 -32.70018155 -50.17484959 -11.05360779 -8.31922127 -21.47610567 -35.55099836 -31.20582214
478 479 480 481 482 483 484 485 486
-28.18153615 -25.14520739 -25.49586541 1.61066211 -21.22419380 -25.14708571 -24.34809075 -18.85538637 -16.31567422
487 488 489 490 491 492 493 494 495
-2.88231965 -18.30373438 -15.62790174 -9.12895259 4.07404195 11.29259920 13.07524351 14.62102370 8.20016993
496 497 498 499 500 501 502 503 504
-7.57731918 -16.67669646 -11.13331918 19.70102370 -4.90161865 -0.13634531 1.65776258 2.08539809 -3.24839102
505 506 507 508 509 510 511 512 513
-11.81559470 43.56360880 22.70511988 21.72961200 12.55585622 8.77273281 5.15834251 28.72939768 21.50864998
514 515 516 517 518 519 520 521 522
-2.16198179 16.00975454 1.24624083 3.03413485 5.66268595 -10.59320460 1.60868322 33.10270602 34.26171751
523 524 525 526 527 528 529 530 531
30.89486300 8.69277965 15.16432338 0.12879229 12.46141049 -47.84513700 4.98127744 35.78611298 13.89432338
532 533 534 535 536 537 538 539 540
7.52127744 -31.56661583 11.35432338 -15.24845213 -16.66915419 -29.22153328 -47.68420499 15.65180005 21.14979501
541 542 543 544 545 546 547 548 549
20.84283272 23.38639336 -1.28153328 -28.63420499 32.83656611 12.47098402 43.07139336 31.56656611 20.80181805
550 551 552 553 554 555 556 557 558
35.19699165 44.76915769 50.84857422 46.50807936 39.77498542 8.48803444 7.93642844 63.44647254 7.83432676
559 560 561 562 563 564 565 566 567
-16.12077428 -15.77531679 -24.12509818 -14.30022771 -15.09556242 -20.76298728 -20.10824828 -28.84574789 -42.43745757
568 569 570 571 572 573 574 575 576
22.71250496 6.33229258 -0.30063045 -14.03044362 -22.02933436 -35.56661130 3.85639498 -31.99952994 -24.60754365
577 578 579 580 581 582 583 584 585
-9.81574815 5.21444663 -10.53423068 -27.19732098 -38.03137470 -45.25599316 -42.94962353 -20.14984309 -13.83477088
586 587 588 589 590 591 592 593 594
-2.54933196 -4.23190894 -5.30717999 4.11980742 -1.93175000 -26.56036026 -44.20436164 -38.71532496 -23.00801045
595 596 597 598 599 600 601 602 603
-39.92054710 -12.36425679 -22.01770605 -8.42003681 4.44221911 -25.68972449 -22.31522586 -20.00886501 -45.84404135
604 605 606 607 608 609 610 611 612
-14.18444756 -29.49972449 5.25825635 -31.20244756 10.38727414 -10.39315072 6.44506017 -4.19826542 -19.68030175
613 614 615 616 617 618 619 620 621
-28.80694427 -19.88318777 -33.97181313 -20.07494477 -30.84295607 -39.97756634 -48.26264464 -43.11413543 -42.71171653
622 623 624 625 626 627 628 629 630
-46.81841985 -33.65546286 -44.57891212 -14.15453991 -27.95905896 -40.02194971 -55.33722664 -3.92368775 -15.79109352
631 632 633 634 635 636 637 638 639
31.05767987 17.74249204 -1.60209132 -6.03138092 -12.17691201 -21.68164699 2.00297892 5.37557315 -8.31232013
640 641 642 643 644 645 646 647 648
-9.35084130 -3.33299828 0.57767987 -7.31884130 -24.77959132 0.80915870 -37.47959132 21.90861908 -3.84974233
649 650 651 652 653 654 655 656 657
0.03224326 -8.83784343 -20.66186629 -24.94811025 -21.26304144 -21.41807567 -27.06109007 -41.85784343 -14.49182348
658 659 660 661 662 663 664 665 666
-34.96685771 0.03012789 -16.67162547 -11.35064833 -14.36689229 10.90817652 -18.88019104 -27.90987211 -11.37995881
667 668 669 670 671 672 673 674 675
-15.16064833 -27.06689229 -48.79956863 -29.45103479 -2.86034488 -14.75685714 -23.15916318 -30.01705373 -19.33720931
676 677 678 679 680 681 682 683 684
-21.51216164 -5.85741742 0.13845917 0.65156887 21.78440149 18.05372979 11.10043416 34.38732814 21.18916756
685 686 687 688 689 690 691 692 693
14.65775283 18.60358546 -14.90285391 29.56839645 -1.94035069 -0.62403993 -2.76893184 40.31399481 -11.13915025
694 695 696 697 698 699 700 701 702
15.65946778 15.89104775 23.75202490 -9.65748359 -16.71285115 -11.34919888 -8.47053222 2.55604775 21.34678881
703 704 705 706 707 708 709 710 711
-4.77207429 -18.68258847 -24.88703024 -30.03242060 -6.02703450 -14.92175588 -19.55531280 -2.23573767 -6.35996162
712 713 714 715 716 717 718 719 720
-13.74749827 -14.13120795 4.02875457 -30.25913617 46.38258552 16.07716066 29.73293670 3.26313273 -21.24243801
721 722 723 724 725 726 727 728 729
-33.53623785 -24.11544878 3.23520989 -17.47444616 -30.82947938 -29.32853322 -14.24377801 -13.70528138 -13.08330423
730 731 732 733 734 735 736 737 738
-24.44531388 -27.62220424 -30.56354150 -21.73625287 -14.03586338 -17.87702909 -25.97327305 -0.06609723 -11.72183634
739 740 741 742 743 744 745 746 747
-25.33275991 -34.91491745 -34.74081033 -14.60920707 -15.72765483 -17.76776065 -28.79446198 -16.76572314 -4.88966459
748 749 750 751 752 753 754 755 756
-11.09881997 -12.25539693 -9.32397916 3.88961143 13.59649802 15.59056591 -0.79645969 -20.39224940 -9.63449730
757 758 759 760 761 762 763 764 765
-24.46274471 -4.88063760 -10.07831000 11.94107089 50.48420278 27.49487279 31.56172632 29.59051089 76.97441727
766 767 768 769 770 771 772 773 774
26.38732089 17.46420278 11.23887279 -4.20035319 -30.81274471 -15.25524471 -24.24813760 0.42807936 8.58771634
775 776 777 778 779 780 781 782 783
8.66732423 12.51488831 -4.61175794 -18.07753305 -14.81192064 38.82203117 19.94157539 -5.37753305 0.93753916
784 785 786 787 788 789 790 791 792
-6.82702192 35.96776097 39.46813592 51.44981890 34.77018343 27.92307982 16.37835312 19.04653592 32.58109431
793 794 795 796 797 798 799 800 801
-6.63822358 -13.75466327 -18.69060693 -15.35829426 -21.15454618 -34.02188273 -39.74467579 -15.16777524 43.55524677
802 803 804 805 806 807 808 809 810
36.84382564 29.42513803 15.68766923 7.99389895 32.12524677 16.88389895 29.58524677 35.16100256 14.34389895
811 812 813 814 815 816 817 818 819
13.64926222 12.22858011 -10.61931161 1.76865037 21.39591697 34.52377985 40.24726884 33.64347973 24.77389510
820 821 822 823 824 825 826 827 828
28.18611016 21.26821575 -15.71121684 16.27221784 34.45932477 24.70259161 32.96966841 20.73721456 -15.28788351
829 830 831 832 833 834 835 836 837
26.43221784 19.62259161 6.93178402 40.14821784 4.39466841 18.36711649 3.28999562 9.69432477 17.71759161
838 839 840 841 842 843 844 845 846
22.17466841 19.67149121 -14.24851111 19.71977921 21.99798615 23.52219704 -1.03491140 -12.07650586 -30.00106694
847 848 849 850 851 852 853 854 855
-9.45864392 15.79853435 7.00367132 -0.85422079 35.71419704 17.16842193 3.16349414 20.97367132 1.68577921
856 857 858 859 860 861 862 863 864
27.54603435 2.43167132 1.53900476 19.09664173 21.57699524 9.96095656 18.80883412 14.86805901 57.79762323
865 866 867 868 869 870 871 872 873
34.68383412 8.73567142 -1.53123706 -11.93069221 -18.81516008 -29.85537781 -4.46350860 2.95990571 0.50706472
874 875 876 877 878 879 880 881 882
10.58685379 3.30562435 -0.31018158 -22.23537781 -30.14262948 -49.61809429 19.56162435 27.18162435 6.19636682
883 884 885 886 887 888 889 890 891
-9.78692213 5.90531494 13.52979530 7.64791807 2.75604393 57.69626505 45.92803861 42.44727559 58.52593694
892 893 894 895 896 897 898 899 900
39.14741553 -15.85936166 20.59681761 11.12835103 14.41367446 6.88794938 7.06712214 2.82170741 -10.22889933
901 902 903 904 905 906 907 908 909
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910 911 912 913 914 915 916 917 918
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919 920 921 922 923 924 925 926 927
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928 929 930 931 932 933 934 935 936
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937 938 939 940 941 942 943 944 945
1.88634356 10.15361686 3.58534848 -3.67335664 20.82582164 24.07737359 25.44957592 30.47889508 16.00361514
946 947 948 949 950 951 952 953 954
8.11098624 14.27826608 14.55237359 16.31026608 22.59570692 11.61073627 -2.99799089 -11.84178957 -23.19758306
955 956 957 958 959 960 961 962 963
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964 965 966 967 968 969 970 971 972
8.09597550 32.80860009 36.94663780 26.15519844 47.67037120 66.20960009 -2.94749355 -14.18228058 -20.75885867
973 974 975 976 977 978 979 980 981
-23.39295503 -18.54738008 15.18858521 14.71490899 -12.04709791 59.98950310 58.50783643 58.08450310 33.36386211
982 983 984 985 986 987 988 989 990
-14.76093251 -22.78050437 -54.54467016 -33.37791421 -37.46349274 -10.86718621 -0.74453434 -10.86718621 3.35793583
991 992 993 994 995 996 997 998 999
-10.86718621 -5.61726642 -10.59706533 11.30167675 23.35399568 17.95524231 17.56121946 20.89497550 20.33808504
1000
3.56962660
[ reached 'max' / getOption("max.print") -- omitted 10228 entries ]residuals(lepmac.lm, quantileFunction = qnorm) 1 2 3 4 5 6 7 8 9
22.23397044 40.32784460 42.50544812 36.51041643 28.56432731 15.51020391 9.99081360 19.68051353 0.14791951
10 11 12 13 14 15 16 17 18
12.57062876 29.55689469 18.62575046 16.95882559 16.96210164 0.04956499 -8.58640250 -30.64438335 -3.31474103
19 20 21 22 23 24 25 26 27
-8.15455419 -31.64744494 2.51041165 -11.65511533 -4.76545516 2.34849855 -2.34941320 -25.05065716 -3.03152587
28 29 30 31 32 33 34 35 36
-22.19299146 -39.08588181 -54.48091604 21.81171420 6.70882268 -2.94360633 -5.67920069 -2.64018284 -51.37230899
37 38 39 40 41 42 43 44 45
4.06672101 -7.52257931 -7.47867097 -10.67531249 26.61581981 24.48515148 1.93332924 -1.37051198 -4.87160110
46 47 48 49 50 51 52 53 54
7.47427550 -3.12511481 -16.29541488 -5.44175360 25.27194036 16.12804981 -14.01052194 3.83383869 1.19173701
55 56 57 58 59 60 61 62 63
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64 65 66 67 68 69 70 71 72
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73 74 75 76 77 78 79 80 81
-8.69791279 -22.86243839 40.40889947 16.82841764 -21.95327993 26.27370592 9.32147782 -41.46641390 29.87391453
82 83 84 85 86 87 88 89 90
20.15866218 18.21013000 11.74540330 2.04936908 33.94723361 28.37013000 -2.30646752 -5.60102218 -13.98823208
91 92 93 94 95 96 97 98 99
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100 101 102 103 104 105 106 107 108
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109 110 111 112 113 114 115 116 117
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118 119 120 121 122 123 124 125 126
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127 128 129 130 131 132 133 134 135
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136 137 138 139 140 141 142 143 144
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145 146 147 148 149 150 151 152 153
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154 155 156 157 158 159 160 161 162
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163 164 165 166 167 168 169 170 171
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172 173 174 175 176 177 178 179 180
19.72088858 18.67997487 8.94249304 31.57406093 4.70953532 19.65755963 23.85510383 26.25610197 28.22693615
181 182 183 184 185 186 187 188 189
8.19177050 21.10554642 40.24960282 35.05160144 36.75017658 -20.71404737 21.07319907 29.93507023 40.92693615
190 191 192 193 194 195 196 197 198
34.54360144 34.21017658 4.38177050 29.07360282 40.80893478 -0.41063293 -6.83587093 -23.10973841 -20.19667639
199 200 201 202 203 204 205 206 207
16.35733700 30.22998926 -2.74929544 15.93400367 22.24053472 34.22923590 16.84896909 59.18598926 36.61207863
208 209 210 211 212 213 214 215 216
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217 218 219 220 221 222 223 224 225
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226 227 228 229 230 231 232 233 234
4.99686124 -1.47030144 -11.26821823 1.81889063 5.63186124 -4.85696810 4.22660918 1.92506587 -10.53719745
235 236 237 238 239 240 241 242 243
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244 245 246 247 248 249 250 251 252
37.60235924 -11.90382302 -4.01444642 -2.50057128 -1.19876719 -5.20340929 0.51282766 -7.64829045 -1.93562044
253 254 255 256 257 258 259 260 261
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262 263 264 265 266 267 268 269 270
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271 272 273 274 275 276 277 278 279
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280 281 282 283 284 285 286 287 288
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289 290 291 292 293 294 295 296 297
26.08664868 47.73633949 -16.56554496 -10.35665607 -16.61545785 0.68664868 43.92633949 7.65989242 -4.66557903
298 299 300 301 302 303 304 305 306
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307 308 309 310 311 312 313 314 315
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316 317 318 319 320 321 322 323 324
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325 326 327 328 329 330 331 332 333
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334 335 336 337 338 339 340 341 342
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343 344 345 346 347 348 349 350 351
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352 353 354 355 356 357 358 359 360
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361 362 363 364 365 366 367 368 369
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370 371 372 373 374 375 376 377 378
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379 380 381 382 383 384 385 386 387
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388 389 390 391 392 393 394 395 396
6.27316552 -13.47519638 -8.25891760 -15.45845198 3.73316552 -6.42398094 28.06510404 24.57364109 19.27549780
397 398 399 400 401 402 403 404 405
25.24847486 14.09070700 13.30108085 5.13800917 7.04300917 20.46175547 -9.54148383 -4.60888959 -25.76544778
406 407 408 409 410 411 412 413 414
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415 416 417 418 419 420 421 422 423
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424 425 426 427 428 429 430 431 432
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433 434 435 436 437 438 439 440 441
23.21331923 31.97033285 40.87690846 31.40863806 27.12772851 19.46485077 2.20271677 15.43826328 28.67495431
442 443 444 445 446 447 448 449 450
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451 452 453 454 455 456 457 458 459
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460 461 462 463 464 465 466 467 468
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469 470 471 472 473 474 475 476 477
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478 479 480 481 482 483 484 485 486
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487 488 489 490 491 492 493 494 495
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496 497 498 499 500 501 502 503 504
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505 506 507 508 509 510 511 512 513
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514 515 516 517 518 519 520 521 522
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523 524 525 526 527 528 529 530 531
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532 533 534 535 536 537 538 539 540
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541 542 543 544 545 546 547 548 549
20.84283272 23.38639336 -1.28153328 -28.63420499 32.83656611 12.47098402 43.07139336 31.56656611 20.80181805
550 551 552 553 554 555 556 557 558
35.19699165 44.76915769 50.84857422 46.50807936 39.77498542 8.48803444 7.93642844 63.44647254 7.83432676
559 560 561 562 563 564 565 566 567
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577 578 579 580 581 582 583 584 585
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595 596 597 598 599 600 601 602 603
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604 605 606 607 608 609 610 611 612
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613 614 615 616 617 618 619 620 621
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622 623 624 625 626 627 628 629 630
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631 632 633 634 635 636 637 638 639
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640 641 642 643 644 645 646 647 648
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649 650 651 652 653 654 655 656 657
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658 659 660 661 662 663 664 665 666
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667 668 669 670 671 672 673 674 675
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676 677 678 679 680 681 682 683 684
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685 686 687 688 689 690 691 692 693
14.65775283 18.60358546 -14.90285391 29.56839645 -1.94035069 -0.62403993 -2.76893184 40.31399481 -11.13915025
694 695 696 697 698 699 700 701 702
15.65946778 15.89104775 23.75202490 -9.65748359 -16.71285115 -11.34919888 -8.47053222 2.55604775 21.34678881
703 704 705 706 707 708 709 710 711
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712 713 714 715 716 717 718 719 720
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721 722 723 724 725 726 727 728 729
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730 731 732 733 734 735 736 737 738
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739 740 741 742 743 744 745 746 747
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748 749 750 751 752 753 754 755 756
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757 758 759 760 761 762 763 764 765
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766 767 768 769 770 771 772 773 774
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775 776 777 778 779 780 781 782 783
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784 785 786 787 788 789 790 791 792
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793 794 795 796 797 798 799 800 801
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802 803 804 805 806 807 808 809 810
36.84382564 29.42513803 15.68766923 7.99389895 32.12524677 16.88389895 29.58524677 35.16100256 14.34389895
811 812 813 814 815 816 817 818 819
13.64926222 12.22858011 -10.61931161 1.76865037 21.39591697 34.52377985 40.24726884 33.64347973 24.77389510
820 821 822 823 824 825 826 827 828
28.18611016 21.26821575 -15.71121684 16.27221784 34.45932477 24.70259161 32.96966841 20.73721456 -15.28788351
829 830 831 832 833 834 835 836 837
26.43221784 19.62259161 6.93178402 40.14821784 4.39466841 18.36711649 3.28999562 9.69432477 17.71759161
838 839 840 841 842 843 844 845 846
22.17466841 19.67149121 -14.24851111 19.71977921 21.99798615 23.52219704 -1.03491140 -12.07650586 -30.00106694
847 848 849 850 851 852 853 854 855
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856 857 858 859 860 861 862 863 864
27.54603435 2.43167132 1.53900476 19.09664173 21.57699524 9.96095656 18.80883412 14.86805901 57.79762323
865 866 867 868 869 870 871 872 873
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874 875 876 877 878 879 880 881 882
10.58685379 3.30562435 -0.31018158 -22.23537781 -30.14262948 -49.61809429 19.56162435 27.18162435 6.19636682
883 884 885 886 887 888 889 890 891
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892 893 894 895 896 897 898 899 900
39.14741553 -15.85936166 20.59681761 11.12835103 14.41367446 6.88794938 7.06712214 2.82170741 -10.22889933
901 902 903 904 905 906 907 908 909
-1.48164393 -24.17764897 29.11124799 -2.02889611 -5.52511565 -12.48831059 4.07939522 -32.94050343 -22.71976014
910 911 912 913 914 915 916 917 918
-28.10846299 -31.69471952 -31.52350671 -28.31093725 -60.02123006 -3.79460478 -23.28850343 11.06194714 -11.20350671
919 920 921 922 923 924 925 926 927
-14.34093725 -9.22123006 -3.54060478 -14.62204157 -25.02910652 -14.76460138 -19.52213977 -2.72201983 -20.86770283
928 929 930 931 932 933 934 935 936
-22.34414324 -11.57835547 -22.66931212 -30.66785241 -5.87795984 -5.98630862 2.41989490 2.99758264 0.89852409
937 938 939 940 941 942 943 944 945
1.88634356 10.15361686 3.58534848 -3.67335664 20.82582164 24.07737359 25.44957592 30.47889508 16.00361514
946 947 948 949 950 951 952 953 954
8.11098624 14.27826608 14.55237359 16.31026608 22.59570692 11.61073627 -2.99799089 -11.84178957 -23.19758306
955 956 957 958 959 960 961 962 963
-34.17829393 -36.00133340 -43.00838728 -37.52478868 -23.17973241 -12.79973253 -12.31753835 -1.77822600 -17.02650296
964 965 966 967 968 969 970 971 972
8.09597550 32.80860009 36.94663780 26.15519844 47.67037120 66.20960009 -2.94749355 -14.18228058 -20.75885867
973 974 975 976 977 978 979 980 981
-23.39295503 -18.54738008 15.18858521 14.71490899 -12.04709791 59.98950310 58.50783643 58.08450310 33.36386211
982 983 984 985 986 987 988 989 990
-14.76093251 -22.78050437 -54.54467016 -33.37791421 -37.46349274 -10.86718621 -0.74453434 -10.86718621 3.35793583
991 992 993 994 995 996 997 998 999
-10.86718621 -5.61726642 -10.59706533 11.30167675 23.35399568 17.95524231 17.56121946 20.89497550 20.33808504
1000
3.56962660
[ reached 'max' / getOption("max.print") -- omitted 10228 entries ]plot(lepmac.lm)
Post-hoc comparisons
lepmac.emm <- emmeans(lepmac.lm, ~ begin_date_year*age_group)
pairs(lepmac.emm, simple = "age_group")begin_date_year = 1989:
contrast estimate SE df t.ratio p.value
age_group0 - age_group1 -21.76 2.880 11201 -7.543 <.0001
age_group0 - age_group2 -52.09 2.830 11201 -18.382 <.0001
age_group0 - age_group3 -80.05 2.820 11201 -28.359 <.0001
age_group0 - age_group4 -104.21 2.820 11201 -36.899 <.0001
age_group0 - age_group5 -124.17 2.830 11201 -43.862 <.0001
age_group0 - age_group6 -139.76 2.850 11201 -49.063 <.0001
age_group0 - age_group7 -152.10 2.880 11201 -52.762 <.0001
age_group0 - age_group8 -163.22 2.970 11201 -54.882 <.0001
age_group0 - age_group9 -175.06 3.180 11201 -55.066 <.0001
age_group0 - age_group10 -184.14 3.550 11201 -51.915 <.0001
age_group0 - age_group11 -196.93 4.560 11201 -43.223 <.0001
age_group1 - age_group2 -30.33 1.060 11201 -28.615 <.0001
age_group1 - age_group3 -58.29 1.030 11201 -56.848 <.0001
age_group1 - age_group4 -82.46 1.030 11201 -80.359 <.0001
age_group1 - age_group5 -102.42 1.040 11201 -98.224 <.0001
age_group1 - age_group6 -118.00 1.090 11201 -108.756 <.0001
age_group1 - age_group7 -130.35 1.170 11201 -111.608 <.0001
age_group1 - age_group8 -141.46 1.370 11201 -102.915 <.0001
age_group1 - age_group9 -153.31 1.770 11201 -86.395 <.0001
age_group1 - age_group10 -162.39 2.370 11201 -68.388 <.0001
age_group1 - age_group11 -175.18 3.720 11201 -47.140 <.0001
age_group2 - age_group3 -27.96 0.860 11201 -32.528 <.0001
age_group2 - age_group4 -52.13 0.860 11201 -60.627 <.0001
age_group2 - age_group5 -72.09 0.879 11201 -82.001 <.0001
age_group2 - age_group6 -87.67 0.928 11201 -94.481 <.0001
age_group2 - age_group7 -100.02 1.020 11201 -97.781 <.0001
age_group2 - age_group8 -111.13 1.250 11201 -88.691 <.0001
age_group2 - age_group9 -122.98 1.680 11201 -73.119 <.0001
age_group2 - age_group10 -132.06 2.310 11201 -57.246 <.0001
age_group2 - age_group11 -144.85 3.670 11201 -39.434 <.0001
age_group3 - age_group4 -24.16 0.814 11201 -29.681 <.0001
age_group3 - age_group5 -44.12 0.834 11201 -52.897 <.0001
age_group3 - age_group6 -59.71 0.885 11201 -67.492 <.0001
age_group3 - age_group7 -72.05 0.983 11201 -73.277 <.0001
age_group3 - age_group8 -83.17 1.220 11201 -68.145 <.0001
age_group3 - age_group9 -95.02 1.660 11201 -57.322 <.0001
age_group3 - age_group10 -104.09 2.290 11201 -45.461 <.0001
age_group3 - age_group11 -116.88 3.660 11201 -31.916 <.0001
age_group4 - age_group5 -19.96 0.833 11201 -23.952 <.0001
age_group4 - age_group6 -35.55 0.884 11201 -40.231 <.0001
age_group4 - age_group7 -47.89 0.982 11201 -48.771 <.0001
age_group4 - age_group8 -59.01 1.220 11201 -48.400 <.0001
age_group4 - age_group9 -70.85 1.660 11201 -42.771 <.0001
age_group4 - age_group10 -79.93 2.290 11201 -34.916 <.0001
age_group4 - age_group11 -92.72 3.660 11201 -25.320 <.0001
age_group5 - age_group6 -15.59 0.902 11201 -17.289 <.0001
age_group5 - age_group7 -27.93 0.998 11201 -27.987 <.0001
age_group5 - age_group8 -39.05 1.230 11201 -31.695 <.0001
age_group5 - age_group9 -50.89 1.670 11201 -30.549 <.0001
age_group5 - age_group10 -59.97 2.300 11201 -26.117 <.0001
age_group5 - age_group11 -72.76 3.670 11201 -19.846 <.0001
age_group6 - age_group7 -12.34 1.040 11201 -11.882 <.0001
age_group6 - age_group8 -23.46 1.260 11201 -18.548 <.0001
age_group6 - age_group9 -35.31 1.690 11201 -20.888 <.0001
age_group6 - age_group10 -44.38 2.310 11201 -19.178 <.0001
age_group6 - age_group11 -57.17 3.680 11201 -15.548 <.0001
age_group7 - age_group8 -11.12 1.330 11201 -8.331 <.0001
age_group7 - age_group9 -22.96 1.740 11201 -13.175 <.0001
age_group7 - age_group10 -32.04 2.350 11201 -13.615 <.0001
age_group7 - age_group11 -44.83 3.700 11201 -12.110 <.0001
age_group8 - age_group9 -11.85 1.890 11201 -6.282 <.0001
age_group8 - age_group10 -20.92 2.460 11201 -8.501 <.0001
age_group8 - age_group11 -33.71 3.770 11201 -8.939 <.0001
age_group9 - age_group10 -9.08 2.700 11201 -3.356 0.0380
age_group9 - age_group11 -21.87 3.930 11201 -5.558 <.0001
age_group10 - age_group11 -12.79 4.240 11201 -3.016 0.1039
P value adjustment: tukey method for comparing a family of 12 estimates test(pairs(lepmac.emm, by = "begin_date_year"), by = NULL, adjust = "mvt") contrast begin_date_year estimate SE df t.ratio p.value
age_group0 - age_group1 1989 -21.76 2.880 11201 -7.543 <.0001
age_group0 - age_group2 1989 -52.09 2.830 11201 -18.382 <.0001
age_group0 - age_group3 1989 -80.05 2.820 11201 -28.359 <.0001
age_group0 - age_group4 1989 -104.21 2.820 11201 -36.899 <.0001
age_group0 - age_group5 1989 -124.17 2.830 11201 -43.862 <.0001
age_group0 - age_group6 1989 -139.76 2.850 11201 -49.063 <.0001
age_group0 - age_group7 1989 -152.10 2.880 11201 -52.762 <.0001
age_group0 - age_group8 1989 -163.22 2.970 11201 -54.882 <.0001
age_group0 - age_group9 1989 -175.06 3.180 11201 -55.066 <.0001
age_group0 - age_group10 1989 -184.14 3.550 11201 -51.915 <.0001
age_group0 - age_group11 1989 -196.93 4.560 11201 -43.223 <.0001
age_group1 - age_group2 1989 -30.33 1.060 11201 -28.615 <.0001
age_group1 - age_group3 1989 -58.29 1.030 11201 -56.848 <.0001
age_group1 - age_group4 1989 -82.46 1.030 11201 -80.359 <.0001
age_group1 - age_group5 1989 -102.42 1.040 11201 -98.224 <.0001
age_group1 - age_group6 1989 -118.00 1.090 11201 -108.756 <.0001
age_group1 - age_group7 1989 -130.35 1.170 11201 -111.608 <.0001
age_group1 - age_group8 1989 -141.46 1.370 11201 -102.915 <.0001
age_group1 - age_group9 1989 -153.31 1.770 11201 -86.395 <.0001
age_group1 - age_group10 1989 -162.39 2.370 11201 -68.388 <.0001
age_group1 - age_group11 1989 -175.18 3.720 11201 -47.140 <.0001
age_group2 - age_group3 1989 -27.96 0.860 11201 -32.528 <.0001
age_group2 - age_group4 1989 -52.13 0.860 11201 -60.627 <.0001
age_group2 - age_group5 1989 -72.09 0.879 11201 -82.001 <.0001
age_group2 - age_group6 1989 -87.67 0.928 11201 -94.481 <.0001
age_group2 - age_group7 1989 -100.02 1.020 11201 -97.781 <.0001
age_group2 - age_group8 1989 -111.13 1.250 11201 -88.691 <.0001
age_group2 - age_group9 1989 -122.98 1.680 11201 -73.119 <.0001
age_group2 - age_group10 1989 -132.06 2.310 11201 -57.246 <.0001
age_group2 - age_group11 1989 -144.85 3.670 11201 -39.434 <.0001
age_group3 - age_group4 1989 -24.16 0.814 11201 -29.681 <.0001
age_group3 - age_group5 1989 -44.12 0.834 11201 -52.897 <.0001
age_group3 - age_group6 1989 -59.71 0.885 11201 -67.492 <.0001
age_group3 - age_group7 1989 -72.05 0.983 11201 -73.277 <.0001
age_group3 - age_group8 1989 -83.17 1.220 11201 -68.145 <.0001
age_group3 - age_group9 1989 -95.02 1.660 11201 -57.322 <.0001
age_group3 - age_group10 1989 -104.09 2.290 11201 -45.461 <.0001
age_group3 - age_group11 1989 -116.88 3.660 11201 -31.916 <.0001
age_group4 - age_group5 1989 -19.96 0.833 11201 -23.952 <.0001
age_group4 - age_group6 1989 -35.55 0.884 11201 -40.231 <.0001
age_group4 - age_group7 1989 -47.89 0.982 11201 -48.771 <.0001
age_group4 - age_group8 1989 -59.01 1.220 11201 -48.400 <.0001
age_group4 - age_group9 1989 -70.85 1.660 11201 -42.771 <.0001
age_group4 - age_group10 1989 -79.93 2.290 11201 -34.916 <.0001
age_group4 - age_group11 1989 -92.72 3.660 11201 -25.320 <.0001
age_group5 - age_group6 1989 -15.59 0.902 11201 -17.289 <.0001
age_group5 - age_group7 1989 -27.93 0.998 11201 -27.987 <.0001
age_group5 - age_group8 1989 -39.05 1.230 11201 -31.695 <.0001
age_group5 - age_group9 1989 -50.89 1.670 11201 -30.549 <.0001
age_group5 - age_group10 1989 -59.97 2.300 11201 -26.117 <.0001
age_group5 - age_group11 1989 -72.76 3.670 11201 -19.846 <.0001
age_group6 - age_group7 1989 -12.34 1.040 11201 -11.882 <.0001
age_group6 - age_group8 1989 -23.46 1.260 11201 -18.548 <.0001
age_group6 - age_group9 1989 -35.31 1.690 11201 -20.888 <.0001
age_group6 - age_group10 1989 -44.38 2.310 11201 -19.178 <.0001
age_group6 - age_group11 1989 -57.17 3.680 11201 -15.548 <.0001
age_group7 - age_group8 1989 -11.12 1.330 11201 -8.331 <.0001
age_group7 - age_group9 1989 -22.96 1.740 11201 -13.175 <.0001
age_group7 - age_group10 1989 -32.04 2.350 11201 -13.615 <.0001
age_group7 - age_group11 1989 -44.83 3.700 11201 -12.110 <.0001
age_group8 - age_group9 1989 -11.85 1.890 11201 -6.282 <.0001
age_group8 - age_group10 1989 -20.92 2.460 11201 -8.501 <.0001
age_group8 - age_group11 1989 -33.71 3.770 11201 -8.939 <.0001
age_group9 - age_group10 1989 -9.08 2.700 11201 -3.356 0.0292
age_group9 - age_group11 1989 -21.87 3.930 11201 -5.558 <.0001
age_group10 - age_group11 1989 -12.79 4.240 11201 -3.016 0.0814
P value adjustment: mvt method for 66 tests #export tables
# #interpret(eta_squared(lepmac.lm), rules = "cohen1992") %>%
# write.csv(file = "Outputs/Tables/lepmac_eta_squared.csv")
#slope contrasts
# Obtain slope estimates
lepmac.slopes <- emtrends(lepmac.lm, ~age_group, var = "begin_date_year")
# Compare estimated slopes to 0
lepmac.slope.contrasts <- test(lepmac.slopes) %>%
mutate(Species = "Bluegill") %>%
rename(Age = age_group)
lepmac.slope.contrasts %>%
write.csv(file = "Outputs/Tables/lepmac_emmeans.csv")Plot raw data
(lepmac.length.year.plot <- ggplot(data = lepmac %>%
mutate(Age = as.factor(age_group)),
aes(x = begin_date_year, y = length_mean_mm,
color = Age, fill = Age, group = Age))+
#geom_point()+
geom_smooth(method = "lm")+
stat_poly_eq(use_label("eq"))+
scale_color_viridis_d()+
scale_fill_viridis_d()+
labs(y = "Length (mm)", x = "Year")+
theme_bw()
)`geom_smooth()` using formula = 'y ~ x'
Marginal Effects
result <- predict_response(lepmac.lm, c("begin_date_year", "age_group"))
# plot(result)
# test_predictions(result) %>%
# write.csv(file = "Outputs/Tables/lepmac_pairwise_length_time_slopes.csv", row.names = F)
(lepmac.marginal.plot <- ggpredict(lepmac.lm, terms = c("begin_date_year", "age_group")) %>%
plot()+
#stat_poly_eq(use_label("eq"))+
stat_regline_equation()+
stat_poly_line()+
# annotate(geom = "text", label = "y = 77 + 0.07x", x = 2000, y = 224)+
# annotate(geom = "text", label = "y = 21 + 0.093x", x = 2000, y = 212)+
# annotate(geom = "text", label = "y = -64 + 0.13x", x = 2000, y = 199)+
# annotate(geom = "text", label = "y = 230 - 0.028x", x = 2000, y = 182)+
# annotate(geom = "text", label = "y = 610 - 0.23x", x = 2000, y = 160)+
# annotate(geom = "text", label = "y = 920 - 0.39x", x = 2000, y = 137)+
# annotate(geom = "text", label = "y = 1200 - 0.55x", x = 2000, y = 110)+
# annotate(geom = "text", label = "y = 1700 - 0.83x", x = 2000, y = 80)+
scale_color_viridis_d()+
scale_fill_viridis_d()+
labs(title = NULL,
y = "Mean Length-at-Age (mm)",
x = "Year")+
guides(fill = guide_legend(title = "Age", reverse = T), color = guide_legend(title = "Age", reverse = T))+
theme_bw()
)Scale for colour is already present.
Adding another scale for colour, which will replace the existing scale.ggsave(filename = "~/GitHub/EAGER_growth/Outputs/Figures/Linear Models/lepmac_marginal_effects_plot.tiff",
lepmac.marginal.plot,
dpi = 300, width = 200, height = 150, units = "mm")
Brown Trout
Model
#filter data for only Black Crappie, Pomooxis nigromaculatus
saltru <- all.grow.merge %>% filter(species == "brown_trout") %>%
filter(age_group %in% c(1, 2, 3, 4, 5), #match age groups in BRTs
!is.na(age_group), !is.na(begin_date_year), !is.na(length_mean_mm),
!is.na(log_max_depth), !is.na(logarea))
#linear model
saltru.lm <- lm(length_mean_mm ~ begin_date_year*age_group + log_max_depth + logarea + doy, data = saltru)
summary(saltru.lm)
Call:
lm(formula = length_mean_mm ~ begin_date_year * age_group + log_max_depth +
logarea + doy, data = saltru)
Residuals:
Min 1Q Median 3Q Max
-213.730 -50.727 0.198 46.483 242.100
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 2365.7783 1967.7017 1.202 0.230283
begin_date_year -1.1273 0.9853 -1.144 0.253602
age_group2 -163.0585 2266.8338 -0.072 0.942708
age_group3 -1358.6741 2231.2552 -0.609 0.543077
age_group4 -1809.9731 2416.6240 -0.749 0.454519
age_group5 -668.1506 2657.7475 -0.251 0.801695
log_max_depth 6.4132 9.2721 0.692 0.489735
logarea 8.6099 2.5278 3.406 0.000758 ***
doy 0.2366 0.0757 3.126 0.001962 **
begin_date_year:age_group2 0.1264 1.1376 0.111 0.911590
begin_date_year:age_group3 0.7729 1.1197 0.690 0.490648
begin_date_year:age_group4 1.0314 1.2138 0.850 0.396212
begin_date_year:age_group5 0.4877 1.3355 0.365 0.715262
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 76.94 on 274 degrees of freedom
(7 observations deleted due to missingness)
Multiple R-squared: 0.6493, Adjusted R-squared: 0.634
F-statistic: 42.28 on 12 and 274 DF, p-value: < 2.2e-16#calculate and interpret effect sizes
eta_squared(saltru.lm) begin_date_year age_group log_max_depth logarea
0.04030015 0.55714211 0.02014525 0.01592787
doy begin_date_year:age_group
0.01402504 0.00177078 #interpret(eta_squared(saltru.lm), rules = "cohen1992")
#calculate AIC score
AIC(saltru.lm)[1] 3322.092#examine model fit
testDispersion(saltru.lm)
DHARMa nonparametric dispersion test via sd of residuals fitted vs. simulated
data: simulationOutput
dispersion = 0.95597, p-value = 0.592
alternative hypothesis: two.sided
simulation.output <- simulateResiduals(fittedModel = saltru.lm)
residuals(saltru.lm) 1 2 3 4 5 6 7 8 9
21.8678104 40.6811565 31.5545026 4.5448582 50.5982283 -56.8884496 -56.4372078 -9.7332195 6.0985907
10 11 12 13 14 15 16 17 18
-41.2371804 -113.4188114 -75.4869458 -7.9242592 -11.6018448 -21.0074584 7.0617408 -4.9978448 0.3796056
19 20 21 22 23 24 25 26 27
65.3067200 36.6611644 49.3611644 47.2028569 34.7839190 24.4545856 -104.2362920 -151.0021524 27.5702485
28 29 30 31 32 33 34 35 36
-187.6388863 -148.0388191 -213.7297515 93.4544470 -145.4988191 -208.6497515 -104.2362920 -131.5288191 5.9802485
37 38 39 40 41 42 43 44 45
66.3611137 -194.2001984 -4.6810570 2.5663747 63.7437538 -23.2790067 46.4483839 2.5074077 55.0777941
46 47 48 49 50 51 52 53 54
53.6048581 -19.1258902 2.0528508 -60.0598956 -40.0383338 -44.4207082 62.3893733 160.9440765 36.4505150
55 56 57 58 59 60 61 62 63
-110.0317852 139.1598975 48.7587906 2.8316238 2.4544388 74.9723897 87.5440072 62.7672854 84.1276035
64 65 66 67 68 69 70 71 72
180.1151362 11.3072772 -26.6682600 76.4398192 28.3042821 115.9261477 70.9665313 93.6647368 -107.3192110
73 75 76 77 78 79 80 81 82
-43.4658954 -92.7067833 47.7699334 -22.1710106 -42.0673095 -51.3303064 25.4436414 7.0197340 -42.7708443
83 84 85 86 87 88 89 90 91
-87.1297370 -93.1471432 -69.3904206 34.5875714 42.9345135 149.5161222 -63.5713746 -29.5419529 -40.8808456
92 93 94 95 96 97 98 99 100
-23.9740663 54.7260318 97.1581578 105.9487137 -0.7477693 12.0489734 -112.1561069 -71.7064787 -147.1234749
101 102 103 104 105 106 107 108 109
-64.2408811 73.8161220 45.0225239 110.7128637 27.8245950 -10.1786623 -39.3964089 20.8421887 32.1254419
110 111 112 113 114 115 116 117 118
88.0601826 9.4169254 -22.0257529 32.3225239 94.4846327 31.5074335 100.4647417 -18.2462782 4.9236294
119 120 121 122 123 124 125 126 127
-65.4733812 68.8009038 65.6370329 22.2498661 -72.3490687 -56.3555915 -62.2207130 -86.0170471 -56.1826666
128 129 130 131 132 133 134 135 136
-4.5765812 21.6957733 -128.5472818 -12.1483455 -5.6789167 36.0448996 -42.1947322 -1.9043923 -93.9676886
137 138 139 140 141 142 143 144 145
-49.0599395 88.1776581 -59.9093114 -39.3839497 28.3040164 59.3241453 -135.8878318 29.6222112 39.7802514
146 147 148 149 150 151 152 153 154
27.9657140 -1.8841324 30.5576985 73.6309449 21.5895027 37.0002389 58.3296606 -49.5894585 75.3071426
155 156 157 158 159 160 161 162 163
84.8432718 31.2961050 -56.0819170 -110.4180026 121.6835096 -56.8815755 -83.9385765 -103.5438946 46.5264057
164 165 166 167 168 169 170 171 172
-77.3786807 -76.0346241 -46.6539337 -91.0769699 -58.9778424 -71.2872113 -15.3104686 -67.3882152 44.4244178
173 174 175 176 177 178 179 180 181
-71.7249623 -81.0135094 -49.5903187 -48.1555175 -47.4285754 -67.0446801 -75.7978710 -24.9011283 56.0032782
182 183 184 185 186 187 188 189 190
-8.5578933 0.1979511 22.5254839 90.4266149 27.8000247 0.6640244 38.9305268 -19.1353720 -69.0523730
191 192 193 194 195 196 197 198 199
-191.4536345 -56.9302993 -97.1696470 30.7808549 182.0462542 160.5016597 148.3701046 5.4631614 121.8374344
200 203 204 205 206 208 209 210 211
121.2592653 69.9268918 99.8287227 -12.8272196 -71.4765652 -33.1826479 -5.6327010 105.6278910 125.3635392
212 213 214 215 216 217 218 219 220
50.3116688 29.7432847 57.6601375 46.5173590 30.7811624 232.0713160 134.9731469 130.7201291 -58.4408412
221 222 223 224 225 226 227 228 229
-14.6375630 -2.6618042 88.9478933 101.0697243 81.5767064 76.4847717 -61.1865996 43.7594858 14.1518403
230 231 232 233 234 235 236 237 238
21.4030314 -48.4609445 -37.2915227 -28.3104155 -57.1878217 40.2291814 -8.8056118 36.9124283 91.5221321
239 240 241 242 243 244 245 246 247
136.7764280 -99.5974071 40.3493812 149.4854839 -23.2416614 14.2849515 21.0965286 21.9235107 91.4514884
248 249 250 251 252 253 254 255 256
65.4337272 43.5002628 -8.3373744 -131.2141373 -9.0572589 134.3608130 -20.6220347 29.9197087 -22.2790783
257 258 262 263 264 265 266 267 268
-16.5689246 -31.5601116 -68.9632410 -103.1164319 -95.3996892 -38.2574358 -59.1929811 -62.9522769 -71.0114671
269 270 271 272 273 274 275 276 277
-126.6404036 -7.6257445 -35.9154046 -29.7751529 -36.6347693 69.8914228 31.1675519 -40.9954046 -50.1233639
278 279 280 281 282 283 284 285 286
2.1015086 65.7154668 39.8951351 9.1768703 -2.4463695 -47.2693664 242.1001395 -91.4981172 -6.9007811
287 288 289 290 291 292 293 294
-39.2162193 -88.0150891 -87.4459241 16.1150709 -30.8620491 -10.4491159 -23.4787030 32.4597087 residuals(saltru.lm, quantileFunction = qnorm) 1 2 3 4 5 6 7 8 9
21.8678104 40.6811565 31.5545026 4.5448582 50.5982283 -56.8884496 -56.4372078 -9.7332195 6.0985907
10 11 12 13 14 15 16 17 18
-41.2371804 -113.4188114 -75.4869458 -7.9242592 -11.6018448 -21.0074584 7.0617408 -4.9978448 0.3796056
19 20 21 22 23 24 25 26 27
65.3067200 36.6611644 49.3611644 47.2028569 34.7839190 24.4545856 -104.2362920 -151.0021524 27.5702485
28 29 30 31 32 33 34 35 36
-187.6388863 -148.0388191 -213.7297515 93.4544470 -145.4988191 -208.6497515 -104.2362920 -131.5288191 5.9802485
37 38 39 40 41 42 43 44 45
66.3611137 -194.2001984 -4.6810570 2.5663747 63.7437538 -23.2790067 46.4483839 2.5074077 55.0777941
46 47 48 49 50 51 52 53 54
53.6048581 -19.1258902 2.0528508 -60.0598956 -40.0383338 -44.4207082 62.3893733 160.9440765 36.4505150
55 56 57 58 59 60 61 62 63
-110.0317852 139.1598975 48.7587906 2.8316238 2.4544388 74.9723897 87.5440072 62.7672854 84.1276035
64 65 66 67 68 69 70 71 72
180.1151362 11.3072772 -26.6682600 76.4398192 28.3042821 115.9261477 70.9665313 93.6647368 -107.3192110
73 75 76 77 78 79 80 81 82
-43.4658954 -92.7067833 47.7699334 -22.1710106 -42.0673095 -51.3303064 25.4436414 7.0197340 -42.7708443
83 84 85 86 87 88 89 90 91
-87.1297370 -93.1471432 -69.3904206 34.5875714 42.9345135 149.5161222 -63.5713746 -29.5419529 -40.8808456
92 93 94 95 96 97 98 99 100
-23.9740663 54.7260318 97.1581578 105.9487137 -0.7477693 12.0489734 -112.1561069 -71.7064787 -147.1234749
101 102 103 104 105 106 107 108 109
-64.2408811 73.8161220 45.0225239 110.7128637 27.8245950 -10.1786623 -39.3964089 20.8421887 32.1254419
110 111 112 113 114 115 116 117 118
88.0601826 9.4169254 -22.0257529 32.3225239 94.4846327 31.5074335 100.4647417 -18.2462782 4.9236294
119 120 121 122 123 124 125 126 127
-65.4733812 68.8009038 65.6370329 22.2498661 -72.3490687 -56.3555915 -62.2207130 -86.0170471 -56.1826666
128 129 130 131 132 133 134 135 136
-4.5765812 21.6957733 -128.5472818 -12.1483455 -5.6789167 36.0448996 -42.1947322 -1.9043923 -93.9676886
137 138 139 140 141 142 143 144 145
-49.0599395 88.1776581 -59.9093114 -39.3839497 28.3040164 59.3241453 -135.8878318 29.6222112 39.7802514
146 147 148 149 150 151 152 153 154
27.9657140 -1.8841324 30.5576985 73.6309449 21.5895027 37.0002389 58.3296606 -49.5894585 75.3071426
155 156 157 158 159 160 161 162 163
84.8432718 31.2961050 -56.0819170 -110.4180026 121.6835096 -56.8815755 -83.9385765 -103.5438946 46.5264057
164 165 166 167 168 169 170 171 172
-77.3786807 -76.0346241 -46.6539337 -91.0769699 -58.9778424 -71.2872113 -15.3104686 -67.3882152 44.4244178
173 174 175 176 177 178 179 180 181
-71.7249623 -81.0135094 -49.5903187 -48.1555175 -47.4285754 -67.0446801 -75.7978710 -24.9011283 56.0032782
182 183 184 185 186 187 188 189 190
-8.5578933 0.1979511 22.5254839 90.4266149 27.8000247 0.6640244 38.9305268 -19.1353720 -69.0523730
191 192 193 194 195 196 197 198 199
-191.4536345 -56.9302993 -97.1696470 30.7808549 182.0462542 160.5016597 148.3701046 5.4631614 121.8374344
200 203 204 205 206 208 209 210 211
121.2592653 69.9268918 99.8287227 -12.8272196 -71.4765652 -33.1826479 -5.6327010 105.6278910 125.3635392
212 213 214 215 216 217 218 219 220
50.3116688 29.7432847 57.6601375 46.5173590 30.7811624 232.0713160 134.9731469 130.7201291 -58.4408412
221 222 223 224 225 226 227 228 229
-14.6375630 -2.6618042 88.9478933 101.0697243 81.5767064 76.4847717 -61.1865996 43.7594858 14.1518403
230 231 232 233 234 235 236 237 238
21.4030314 -48.4609445 -37.2915227 -28.3104155 -57.1878217 40.2291814 -8.8056118 36.9124283 91.5221321
239 240 241 242 243 244 245 246 247
136.7764280 -99.5974071 40.3493812 149.4854839 -23.2416614 14.2849515 21.0965286 21.9235107 91.4514884
248 249 250 251 252 253 254 255 256
65.4337272 43.5002628 -8.3373744 -131.2141373 -9.0572589 134.3608130 -20.6220347 29.9197087 -22.2790783
257 258 262 263 264 265 266 267 268
-16.5689246 -31.5601116 -68.9632410 -103.1164319 -95.3996892 -38.2574358 -59.1929811 -62.9522769 -71.0114671
269 270 271 272 273 274 275 276 277
-126.6404036 -7.6257445 -35.9154046 -29.7751529 -36.6347693 69.8914228 31.1675519 -40.9954046 -50.1233639
278 279 280 281 282 283 284 285 286
2.1015086 65.7154668 39.8951351 9.1768703 -2.4463695 -47.2693664 242.1001395 -91.4981172 -6.9007811
287 288 289 290 291 292 293 294
-39.2162193 -88.0150891 -87.4459241 16.1150709 -30.8620491 -10.4491159 -23.4787030 32.4597087 plot(saltru.lm)
Post-hoc comparisons
saltru.emm <- emmeans(saltru.lm, ~ begin_date_year*age_group)
pairs(saltru.emm, simple = "age_group")begin_date_year = 1987:
contrast estimate SE df t.ratio p.value
age_group1 - age_group2 -88.2 17.0 274 -5.193 <.0001
age_group1 - age_group3 -177.1 16.9 274 -10.483 <.0001
age_group1 - age_group4 -239.6 17.4 274 -13.753 <.0001
age_group1 - age_group5 -301.0 19.5 274 -15.446 <.0001
age_group2 - age_group3 -88.9 12.7 274 -7.016 <.0001
age_group2 - age_group4 -151.4 13.4 274 -11.334 <.0001
age_group2 - age_group5 -212.8 16.0 274 -13.280 <.0001
age_group3 - age_group4 -62.5 13.2 274 -4.728 <.0001
age_group3 - age_group5 -123.9 15.9 274 -7.792 <.0001
age_group4 - age_group5 -61.4 16.5 274 -3.731 0.0021
P value adjustment: tukey method for comparing a family of 5 estimates test(pairs(saltru.emm, by = "begin_date_year"), by = NULL, adjust = "mvt") contrast begin_date_year estimate SE df t.ratio p.value
age_group1 - age_group2 1987 -88.2 17.0 274 -5.193 <.0001
age_group1 - age_group3 1987 -177.1 16.9 274 -10.483 <.0001
age_group1 - age_group4 1987 -239.6 17.4 274 -13.753 <.0001
age_group1 - age_group5 1987 -301.0 19.5 274 -15.446 <.0001
age_group2 - age_group3 1987 -88.9 12.7 274 -7.016 <.0001
age_group2 - age_group4 1987 -151.4 13.4 274 -11.334 <.0001
age_group2 - age_group5 1987 -212.8 16.0 274 -13.280 <.0001
age_group3 - age_group4 1987 -62.5 13.2 274 -4.728 <.0001
age_group3 - age_group5 1987 -123.9 15.9 274 -7.792 <.0001
age_group4 - age_group5 1987 -61.4 16.5 274 -3.731 0.0022
P value adjustment: mvt method for 10 tests #export tables
# #interpret(eta_squared(saltru.lm), rules = "cohen1992") %>%
# write.csv(file = "Outputs/Tables/saltru_eta_squared.csv")
#slope contrasts
# Obtain slope estimates
saltru.slopes <- emtrends(saltru.lm, ~age_group, var = "begin_date_year")
# Compare estimated slopes to 0
saltru.slope.contrasts <- test(saltru.slopes) %>%
mutate(Species = "Brown Trout") %>%
rename(Age = age_group)
saltru.slope.contrasts %>%
write.csv(file = "Outputs/Tables/saltru_emmeans.csv")Plot raw data
(saltru.length.year.plot <- ggplot(data = saltru %>%
mutate(Age = as.factor(age_group)),
aes(x = begin_date_year, y = length_mean_mm,
color = Age, fill = Age, group = Age))+
#geom_point()+
geom_smooth(method = "lm")+
stat_poly_eq(use_label("eq"))+
scale_color_viridis_d()+
scale_fill_viridis_d()+
labs(y = "Length (mm)", x = "Year")+
theme_bw()
)`geom_smooth()` using formula = 'y ~ x'
Marginal Effects
result <- predict_response(saltru.lm, c("begin_date_year", "age_group"))
# plot(result)
# test_predictions(result) %>%
# write.csv(file = "Outputs/Tables/saltru_pairwise_length_time_slopes.csv", row.names = F)
(saltru.marginal.plot <- ggpredict(saltru.lm, terms = c("begin_date_year", "age_group")) %>%
plot()+
#stat_poly_eq(use_label("eq"))+
stat_regline_equation()+
stat_poly_line()+
# annotate(geom = "text", label = "y = 3000 - 1.2x", x = 2000, y = 250)+
# annotate(geom = "text", label = "y = 2000 - 0.76x", x = 2000, y = 350)+
# annotate(geom = "text", label = "y = 1900 - 0.76x", x = 2000, y = 430)+
# annotate(geom = "text", label = "y = 3200 - 1.4x", x = 2000, y = 500)+
# annotate(geom = "text", label = "y = 4100 - 1.9x", x = 2000, y = 550)+
scale_color_viridis_d()+
scale_fill_viridis_d()+
labs(title = NULL,
y = "Mean Length-at-Age (mm)",
x = "Year")+
guides(fill = guide_legend(title = "Age", reverse = T), color = guide_legend(title = "Age", reverse = T))+
theme_bw()
)Scale for colour is already present.
Adding another scale for colour, which will replace the existing scale.ggsave(filename = "~/GitHub/EAGER_growth/Outputs/Figures/Linear Models/saltru_marginal_effects_plot.tiff",
saltru.marginal.plot,
dpi = 300, width = 200, height = 150, units = "mm")
Cisco
Model
#filter data for only Black Crappie, Pomooxis nigromaculatus
corart <- all.grow.merge %>% filter(species == "cisco") %>%
filter(age_group %in% c(2, 3, 4, 5, 6, 7), #match age groups in BRTs
!is.na(age_group), !is.na(begin_date_year), !is.na(length_mean_mm),
!is.na(log_max_depth), !is.na(logarea))
#linear model
corart.lm <- lm(length_mean_mm ~ begin_date_year*age_group + log_max_depth + logarea + doy, data = corart)
summary(corart.lm)
Call:
lm(formula = length_mean_mm ~ begin_date_year * age_group + log_max_depth +
logarea + doy, data = corart)
Residuals:
Min 1Q Median 3Q Max
-114.179 -21.808 0.634 20.069 151.328
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 7.154e+02 5.518e+02 1.296 0.19559
begin_date_year -2.308e-01 2.789e-01 -0.828 0.40833
age_group3 -2.205e+02 7.287e+02 -0.303 0.76231
age_group4 -1.677e+02 6.777e+02 -0.247 0.80475
age_group5 -1.581e+03 7.498e+02 -2.108 0.03561 *
age_group6 -5.483e+02 7.203e+02 -0.761 0.44700
age_group7 3.134e+02 8.959e+02 0.350 0.72668
log_max_depth -1.178e+01 3.999e+00 -2.945 0.00342 **
logarea -2.261e-01 1.055e+00 -0.214 0.83034
doy 1.231e-01 2.362e-02 5.213 2.97e-07 ***
begin_date_year:age_group3 1.285e-01 3.689e-01 0.348 0.72768
begin_date_year:age_group4 1.198e-01 3.428e-01 0.350 0.72688
begin_date_year:age_group5 8.491e-01 3.793e-01 2.238 0.02574 *
begin_date_year:age_group6 3.359e-01 3.642e-01 0.922 0.35698
begin_date_year:age_group7 -8.665e-02 4.526e-01 -0.191 0.84827
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 38.23 on 402 degrees of freedom
(10 observations deleted due to missingness)
Multiple R-squared: 0.5708, Adjusted R-squared: 0.5558
F-statistic: 38.18 on 14 and 402 DF, p-value: < 2.2e-16#calculate and interpret effect sizes
eta_squared(corart.lm) begin_date_year age_group log_max_depth logarea
0.001873808 0.515170771 0.009739229 0.005740233
doy begin_date_year:age_group
0.029645902 0.008585668 #interpret(eta_squared(corart.lm), rules = "cohen1992")
#calculate AIC score
AIC(corart.lm)[1] 4238.8#examine model fit
testDispersion(corart.lm)
DHARMa nonparametric dispersion test via sd of residuals fitted vs. simulated
data: simulationOutput
dispersion = 0.96609, p-value = 0.592
alternative hypothesis: two.sided
simulation.output <- simulateResiduals(fittedModel = corart.lm)
residuals(corart.lm) 1 2 3 4 5 6 7 8 9
-4.6446002 -27.1202323 -71.3642885 -42.5952717 -14.0136740 -32.5933502 24.1166557 6.5002941 -19.1441261
10 11 12 13 14 15 16 17 18
-34.8480990 -34.3312849 -52.4402374 -67.9950923 29.5056316 33.6215712 22.6149046 -10.8016644 -13.6261705
19 20 21 22 23 24 25 26 27
-18.4216644 4.4189538 1.5939867 -19.2791131 -23.0783311 -7.7193467 -6.0061705 -21.8083311 -36.2210462
28 29 30 31 32 33 34 35 36
10.0606533 -58.0958514 79.9218180 69.6036343 20.2903731 -2.5981954 28.4258760 117.0774209 57.5565721
37 38 39 40 41 42 43 44 45
-16.5681954 -27.7025791 46.1265721 -15.9331954 58.2185407 25.2411420 48.6434597 -28.9563067 33.7516674
46 47 48 49 50 51 52 53 54
27.6951568 31.3226838 27.0188538 2.2503763 5.5487270 -25.6896237 13.1687270 -43.1013448 -10.4496237
55 56 57 58 59 60 61 62 63
-24.6970427 -53.0876257 -62.0504611 -80.0382955 -53.2992924 -47.3184611 -7.7911615 -7.7563345 -20.5908275
64 65 66 67 68 69 70 71 72
-13.5454166 35.3674038 13.6006458 15.2013094 56.0984451 -45.0804044 -24.4735299 -32.0534400 -92.9333593
73 74 75 76 77 78 79 80 81
-59.4992388 -67.1140655 -86.9769188 -67.2786925 -15.4167833 -25.7374638 3.8157467 -4.0174642 -32.0451633
82 83 84 85 86 87 88 89 90
15.2101784 -15.3432106 -49.7055419 -62.2757300 -83.2698391 -82.0949619 -17.9532406 -1.3685111 -38.2817969
91 92 93 94 95 96 97 98 99
29.9174763 93.0926455 87.9217982 73.5408765 69.8135339 15.7141030 20.5897768 23.3093199 23.0699412
100 103 104 105 106 107 113 114 115
13.2496997 0.7411978 13.2281424 6.5169520 8.3043398 3.1415898 -2.6512956 -15.4683303 -17.5014524
116 117 118 119 120 121 122 123 124
-10.0684956 -23.2836523 -5.1770287 -25.4394805 8.7420671 14.0547076 -7.6231878 10.7318522 5.4999008
125 126 127 128 129 130 131 132 133
39.4038448 15.2326863 5.3736435 2.7577128 1.7597251 13.8453166 -0.6522807 26.8537764 58.7776276
134 135 136 137 138 139 140 141 142
-46.0095766 55.3804065 -43.4667762 -72.7600308 29.3947940 44.4741135 38.4673240 -4.9258869 -12.6335860
143 144 145 146 147 148 149 150 151
-6.8210732 -22.5144570 -27.6765752 -53.6208724 -54.4419282 -73.1984490 -66.2263903 -45.1589707 1.4510335
152 153 154 155 156 157 158 159 160
-12.2254872 -10.3334286 -24.8260090 -12.6651439 -16.1576564 15.7258781 17.6179367 8.2053564 -18.2348828
161 162 163 164 165 166 167 168 169
0.6343691 13.1475012 6.7885112 -10.2044003 -44.5796249 5.6703388 -9.7914457 7.0989894 -9.5160316
170 171 172 173 174 175 176 177 178
-18.2214932 -51.4458195 34.2466575 44.8531445 22.0045998 19.0066575 44.8531445 13.9847909 -9.0620770
179 180 181 182 183 184 185 186 187
-10.3410669 -2.3444609 16.5247909 1.0979230 -10.3410669 -12.0939785 29.7779424 4.2172620 -1.7895275
188 189 190 191 192 193 194 195 196
11.8372620 8.3704725 16.8733488 -1.0847900 -5.6330464 24.4933488 6.5352100 4.5269536 -19.5727244
197 198 199 200 201 202 203 204 205
16.2993636 -9.2962335 -30.1659569 3.5993636 -11.8362335 -14.9259569 -24.8121020 -40.5148673 -49.2310160
206 207 208 209 210 211 212 213 214
-2.9172801 5.1598666 14.6980929 7.3980549 15.7566884 -15.9791763 7.8104631 5.2669073 11.5333205
215 216 217 218 219 220 221 222 223
-2.3016867 -19.4774213 -27.6793581 -36.7339170 -25.8703241 4.4489954 23.6289950 0.2753235 1.5906218
224 225 226 227 228 229 230 231 232
1.5608786 -10.3049087 9.8803835 -21.9696884 0.5051879 26.3206153 13.6519761 14.4613233 58.5592502
233 234 235 236 237 238 239 240 241
35.8658130 66.0209821 22.7501348 3.2892132 7.1818706 -2.2257607 35.9889435 66.1441127 22.8732654
242 243 244 245 246 247 248 249 250
3.4123437 7.3050011 -2.1026301 -41.8748904 -38.6312974 -36.2519779 10.8680217 0.9212326 26.0203225
251 252 253 254 255 256 257 258 259
-41.5054988 -38.2619058 -35.8825863 1.2906242 -4.0025867 26.3897141 18.2245282 -4.2211557 -6.8696958
260 261 262 263 264 265 266 267 268
4.0327747 9.1586348 -11.1736501 -32.1179704 122.2447873 146.2468189 86.2318625 -13.1253364 -36.2126739
269 270 271 272 273 274 275 276 277
-8.1976673 67.4088043 42.3637535 2.0413502 -36.2806287 -27.7165341 -26.5565041 18.5819037 47.9433142
278 279 280 281 282 283 284 285 286
-4.3177285 -1.7987426 -8.3335426 17.6104379 -10.3069305 -68.9062128 20.0687937 52.7918083 74.0559029
287 288 289 290 291 292 293 294 295
57.4359328 -9.5970545 -51.6304355 -58.8980775 -59.4181833 -85.6676484 -22.1450168 -30.7664031 -37.9817098
296 297 298 299 303 304 305 306 307
-8.8767307 -20.9411453 -24.4079348 56.6588542 37.6678478 -7.7342182 -10.4087112 30.5608072 31.8218172
308 309 310 311 312 313 314 315 316
35.1489057 30.1188043 33.3623972 50.9817168 -8.1950693 -0.7444789 21.9179981 64.3715536 -50.2828540
317 318 319 320 321 322 323 324 325
-31.8164181 -11.7685380 -4.2570781 -12.0061209 43.8965107 -2.8248756 22.9798178 13.9847968 55.0390336
326 327 328 329 330 331 332 333 334
60.6630140 53.0656456 34.8282237 13.1546369 47.0923618 47.0099894 44.7850023 -1.8431928 -37.6007158
335 336 337 338 339 340 341 342 343
-56.1071602 50.7896483 36.5103472 3.5460358 -6.6175200 -43.5311068 143.6282448 151.3283254 80.3051603
344 345 346 347 348 349 350 351 352
39.7853309 43.7564914 45.7664873 -9.3546425 -16.5530554 -13.6463511 12.2831993 38.2133750 -21.7333261
353 354 355 356 357 358 359 360 361
13.4935688 44.4402445 1.0035434 31.8053522 32.7199057 7.5630746 51.7777829 47.0669098 -3.1258025
362 363 364 365 366 367 368 369 370
60.4056911 -1.9606275 -5.9836828 9.6571268 17.0325146 -2.1002354 -2.9278107 22.2678631 27.2880275
371 372 373 374 375 376 377 378 379
27.6277860 24.9664482 25.6087886 22.4016651 25.7182864 -0.9223995 8.0793177 19.0751819 25.2418679
380 381 382 383 384 385 386 387 388
20.7691559 12.2189144 10.0805928 9.0295999 16.6598096 11.7051489 2.6131591 12.6667956 17.2191966
389 390 391 392 393 394 395 396 397
16.9600124 15.1623095 15.3388367 -11.8248571 -30.9348681 -11.5121500 -9.5151429 15.4663616 -62.2817471
398 399 400 401 402 403 404 405 406
-81.7382885 -45.4176025 -80.5374772 -114.1790139 -64.0739756 16.0957658 5.3318880 15.7337963 -5.1115623
407 408 409 410 411 412 413 414 415
9.7027565 -91.5140369 10.1184408 -44.1419025 -15.4046897 11.1903608 -28.5269571 4.7559250 -88.1578643
416 417 418 419 420 421 422 423 424
-44.6288708 -17.0053866 -33.4363679 -24.3882265 -55.6731741 -24.7924850 -32.7128318 -16.8913571 -19.2006155
425 426 427
-30.2959623 -29.7144876 -53.3794351 residuals(corart.lm, quantileFunction = qnorm) 1 2 3 4 5 6 7 8 9
-4.6446002 -27.1202323 -71.3642885 -42.5952717 -14.0136740 -32.5933502 24.1166557 6.5002941 -19.1441261
10 11 12 13 14 15 16 17 18
-34.8480990 -34.3312849 -52.4402374 -67.9950923 29.5056316 33.6215712 22.6149046 -10.8016644 -13.6261705
19 20 21 22 23 24 25 26 27
-18.4216644 4.4189538 1.5939867 -19.2791131 -23.0783311 -7.7193467 -6.0061705 -21.8083311 -36.2210462
28 29 30 31 32 33 34 35 36
10.0606533 -58.0958514 79.9218180 69.6036343 20.2903731 -2.5981954 28.4258760 117.0774209 57.5565721
37 38 39 40 41 42 43 44 45
-16.5681954 -27.7025791 46.1265721 -15.9331954 58.2185407 25.2411420 48.6434597 -28.9563067 33.7516674
46 47 48 49 50 51 52 53 54
27.6951568 31.3226838 27.0188538 2.2503763 5.5487270 -25.6896237 13.1687270 -43.1013448 -10.4496237
55 56 57 58 59 60 61 62 63
-24.6970427 -53.0876257 -62.0504611 -80.0382955 -53.2992924 -47.3184611 -7.7911615 -7.7563345 -20.5908275
64 65 66 67 68 69 70 71 72
-13.5454166 35.3674038 13.6006458 15.2013094 56.0984451 -45.0804044 -24.4735299 -32.0534400 -92.9333593
73 74 75 76 77 78 79 80 81
-59.4992388 -67.1140655 -86.9769188 -67.2786925 -15.4167833 -25.7374638 3.8157467 -4.0174642 -32.0451633
82 83 84 85 86 87 88 89 90
15.2101784 -15.3432106 -49.7055419 -62.2757300 -83.2698391 -82.0949619 -17.9532406 -1.3685111 -38.2817969
91 92 93 94 95 96 97 98 99
29.9174763 93.0926455 87.9217982 73.5408765 69.8135339 15.7141030 20.5897768 23.3093199 23.0699412
100 103 104 105 106 107 113 114 115
13.2496997 0.7411978 13.2281424 6.5169520 8.3043398 3.1415898 -2.6512956 -15.4683303 -17.5014524
116 117 118 119 120 121 122 123 124
-10.0684956 -23.2836523 -5.1770287 -25.4394805 8.7420671 14.0547076 -7.6231878 10.7318522 5.4999008
125 126 127 128 129 130 131 132 133
39.4038448 15.2326863 5.3736435 2.7577128 1.7597251 13.8453166 -0.6522807 26.8537764 58.7776276
134 135 136 137 138 139 140 141 142
-46.0095766 55.3804065 -43.4667762 -72.7600308 29.3947940 44.4741135 38.4673240 -4.9258869 -12.6335860
143 144 145 146 147 148 149 150 151
-6.8210732 -22.5144570 -27.6765752 -53.6208724 -54.4419282 -73.1984490 -66.2263903 -45.1589707 1.4510335
152 153 154 155 156 157 158 159 160
-12.2254872 -10.3334286 -24.8260090 -12.6651439 -16.1576564 15.7258781 17.6179367 8.2053564 -18.2348828
161 162 163 164 165 166 167 168 169
0.6343691 13.1475012 6.7885112 -10.2044003 -44.5796249 5.6703388 -9.7914457 7.0989894 -9.5160316
170 171 172 173 174 175 176 177 178
-18.2214932 -51.4458195 34.2466575 44.8531445 22.0045998 19.0066575 44.8531445 13.9847909 -9.0620770
179 180 181 182 183 184 185 186 187
-10.3410669 -2.3444609 16.5247909 1.0979230 -10.3410669 -12.0939785 29.7779424 4.2172620 -1.7895275
188 189 190 191 192 193 194 195 196
11.8372620 8.3704725 16.8733488 -1.0847900 -5.6330464 24.4933488 6.5352100 4.5269536 -19.5727244
197 198 199 200 201 202 203 204 205
16.2993636 -9.2962335 -30.1659569 3.5993636 -11.8362335 -14.9259569 -24.8121020 -40.5148673 -49.2310160
206 207 208 209 210 211 212 213 214
-2.9172801 5.1598666 14.6980929 7.3980549 15.7566884 -15.9791763 7.8104631 5.2669073 11.5333205
215 216 217 218 219 220 221 222 223
-2.3016867 -19.4774213 -27.6793581 -36.7339170 -25.8703241 4.4489954 23.6289950 0.2753235 1.5906218
224 225 226 227 228 229 230 231 232
1.5608786 -10.3049087 9.8803835 -21.9696884 0.5051879 26.3206153 13.6519761 14.4613233 58.5592502
233 234 235 236 237 238 239 240 241
35.8658130 66.0209821 22.7501348 3.2892132 7.1818706 -2.2257607 35.9889435 66.1441127 22.8732654
242 243 244 245 246 247 248 249 250
3.4123437 7.3050011 -2.1026301 -41.8748904 -38.6312974 -36.2519779 10.8680217 0.9212326 26.0203225
251 252 253 254 255 256 257 258 259
-41.5054988 -38.2619058 -35.8825863 1.2906242 -4.0025867 26.3897141 18.2245282 -4.2211557 -6.8696958
260 261 262 263 264 265 266 267 268
4.0327747 9.1586348 -11.1736501 -32.1179704 122.2447873 146.2468189 86.2318625 -13.1253364 -36.2126739
269 270 271 272 273 274 275 276 277
-8.1976673 67.4088043 42.3637535 2.0413502 -36.2806287 -27.7165341 -26.5565041 18.5819037 47.9433142
278 279 280 281 282 283 284 285 286
-4.3177285 -1.7987426 -8.3335426 17.6104379 -10.3069305 -68.9062128 20.0687937 52.7918083 74.0559029
287 288 289 290 291 292 293 294 295
57.4359328 -9.5970545 -51.6304355 -58.8980775 -59.4181833 -85.6676484 -22.1450168 -30.7664031 -37.9817098
296 297 298 299 303 304 305 306 307
-8.8767307 -20.9411453 -24.4079348 56.6588542 37.6678478 -7.7342182 -10.4087112 30.5608072 31.8218172
308 309 310 311 312 313 314 315 316
35.1489057 30.1188043 33.3623972 50.9817168 -8.1950693 -0.7444789 21.9179981 64.3715536 -50.2828540
317 318 319 320 321 322 323 324 325
-31.8164181 -11.7685380 -4.2570781 -12.0061209 43.8965107 -2.8248756 22.9798178 13.9847968 55.0390336
326 327 328 329 330 331 332 333 334
60.6630140 53.0656456 34.8282237 13.1546369 47.0923618 47.0099894 44.7850023 -1.8431928 -37.6007158
335 336 337 338 339 340 341 342 343
-56.1071602 50.7896483 36.5103472 3.5460358 -6.6175200 -43.5311068 143.6282448 151.3283254 80.3051603
344 345 346 347 348 349 350 351 352
39.7853309 43.7564914 45.7664873 -9.3546425 -16.5530554 -13.6463511 12.2831993 38.2133750 -21.7333261
353 354 355 356 357 358 359 360 361
13.4935688 44.4402445 1.0035434 31.8053522 32.7199057 7.5630746 51.7777829 47.0669098 -3.1258025
362 363 364 365 366 367 368 369 370
60.4056911 -1.9606275 -5.9836828 9.6571268 17.0325146 -2.1002354 -2.9278107 22.2678631 27.2880275
371 372 373 374 375 376 377 378 379
27.6277860 24.9664482 25.6087886 22.4016651 25.7182864 -0.9223995 8.0793177 19.0751819 25.2418679
380 381 382 383 384 385 386 387 388
20.7691559 12.2189144 10.0805928 9.0295999 16.6598096 11.7051489 2.6131591 12.6667956 17.2191966
389 390 391 392 393 394 395 396 397
16.9600124 15.1623095 15.3388367 -11.8248571 -30.9348681 -11.5121500 -9.5151429 15.4663616 -62.2817471
398 399 400 401 402 403 404 405 406
-81.7382885 -45.4176025 -80.5374772 -114.1790139 -64.0739756 16.0957658 5.3318880 15.7337963 -5.1115623
407 408 409 410 411 412 413 414 415
9.7027565 -91.5140369 10.1184408 -44.1419025 -15.4046897 11.1903608 -28.5269571 4.7559250 -88.1578643
416 417 418 419 420 421 422 423 424
-44.6288708 -17.0053866 -33.4363679 -24.3882265 -55.6731741 -24.7924850 -32.7128318 -16.8913571 -19.2006155
425 426 427
-30.2959623 -29.7144876 -53.3794351 plot(corart.lm)
Post-hoc comparisons
corart.emm <- emmeans(corart.lm, ~ begin_date_year*age_group)
pairs(corart.emm, simple = "age_group")begin_date_year = 1978:
contrast estimate SE df t.ratio p.value
age_group2 - age_group3 -33.7 6.79 402 -4.970 <.0001
age_group2 - age_group4 -69.4 6.60 402 -10.510 <.0001
age_group2 - age_group5 -98.7 6.93 402 -14.252 <.0001
age_group2 - age_group6 -116.1 7.16 402 -16.205 <.0001
age_group2 - age_group7 -142.0 7.89 402 -17.997 <.0001
age_group3 - age_group4 -35.6 5.82 402 -6.118 <.0001
age_group3 - age_group5 -65.0 6.19 402 -10.502 <.0001
age_group3 - age_group6 -82.3 6.42 402 -12.832 <.0001
age_group3 - age_group7 -108.2 7.19 402 -15.054 <.0001
age_group4 - age_group5 -29.4 5.97 402 -4.917 <.0001
age_group4 - age_group6 -46.7 6.21 402 -7.521 <.0001
age_group4 - age_group7 -72.6 7.01 402 -10.356 <.0001
age_group5 - age_group6 -17.4 6.55 402 -2.652 0.0876
age_group5 - age_group7 -43.2 7.30 402 -5.918 <.0001
age_group6 - age_group7 -25.9 7.41 402 -3.492 0.0070
P value adjustment: tukey method for comparing a family of 6 estimates test(pairs(corart.emm, by = "begin_date_year"), by = NULL, adjust = "mvt") contrast begin_date_year estimate SE df t.ratio p.value
age_group2 - age_group3 1978 -33.7 6.79 402 -4.970 <.0001
age_group2 - age_group4 1978 -69.4 6.60 402 -10.510 <.0001
age_group2 - age_group5 1978 -98.7 6.93 402 -14.252 <.0001
age_group2 - age_group6 1978 -116.1 7.16 402 -16.205 <.0001
age_group2 - age_group7 1978 -142.0 7.89 402 -17.997 <.0001
age_group3 - age_group4 1978 -35.6 5.82 402 -6.118 <.0001
age_group3 - age_group5 1978 -65.0 6.19 402 -10.502 <.0001
age_group3 - age_group6 1978 -82.3 6.42 402 -12.832 <.0001
age_group3 - age_group7 1978 -108.2 7.19 402 -15.054 <.0001
age_group4 - age_group5 1978 -29.4 5.97 402 -4.917 <.0001
age_group4 - age_group6 1978 -46.7 6.21 402 -7.521 <.0001
age_group4 - age_group7 1978 -72.6 7.01 402 -10.356 <.0001
age_group5 - age_group6 1978 -17.4 6.55 402 -2.652 0.0864
age_group5 - age_group7 1978 -43.2 7.30 402 -5.918 <.0001
age_group6 - age_group7 1978 -25.9 7.41 402 -3.492 0.0070
P value adjustment: mvt method for 15 tests #export tables
# #interpret(eta_squared(corart.lm), rules = "cohen1992") %>%
# write.csv(file = "Outputs/Tables/corart_eta_squared.csv")
#slope contrasts
# Obtain slope estimates
corart.slopes <- emtrends(corart.lm, ~age_group, var = "begin_date_year")
# Compare estimated slopes to 0
corart.slope.contrasts <- test(corart.slopes) %>%
mutate(Species = "Cisco") %>%
rename(Age = age_group)
corart.slope.contrasts %>%
write.csv(file = "Outputs/Tables/corart_emmeans.csv")Plot raw data
(corart.length.year.plot <- ggplot(data = corart %>%
mutate(Age = as.factor(age_group)),
aes(x = begin_date_year, y = length_mean_mm,
color = Age, fill = Age, group = Age))+
#geom_point()+
geom_smooth(method = "lm")+
stat_poly_eq(use_label("eq"))+
scale_color_viridis_d()+
scale_fill_viridis_d()+
labs(y = "Length (mm)", x = "Year")+
theme_bw()
)`geom_smooth()` using formula = 'y ~ x'
Marginal Effects
result <- predict_response(corart.lm, c("begin_date_year", "age_group"))
# plot(result)
# test_predictions(result) %>%
# write.csv(file = "Outputs/Tables/corart_pairwise_length_time_slopes.csv", row.names = F)
(corart.marginal.plot <- ggpredict(corart.lm, terms = c("begin_date_year", "age_group")) %>%
plot()+
#stat_poly_eq(use_label("eq"))+
stat_regline_equation()+
stat_poly_line()+
# annotate(geom = "text", label = "y = 1100 + 0.39x", x = 1970, y = 400)+
# annotate(geom = "text", label = "y = 340 + 0.01x", x = 1970, y = 375)+
# annotate(geom = "text", label = "y = -740 + 0.55x", x = 1970, y = 350)+
# annotate(geom = "text", label = "y = 1400 - 0.57x", x = 1980, y = 325)+
# annotate(geom = "text", label = "y = 860 - 0.29x", x = 1970, y = 310)+
# annotate(geom = "text", label = "y = 1700 - 0.75x", x = 1970, y = 275)+
scale_color_viridis_d()+
scale_fill_viridis_d()+
labs(title = NULL,
y = "Mean Length-at-Age (mm)",
x = "Year")+
guides(fill = guide_legend(title = "Age", reverse = T), color = guide_legend(title = "Age", reverse = T))+
theme_bw()
)Scale for colour is already present.
Adding another scale for colour, which will replace the existing scale.ggsave(filename = "~/GitHub/EAGER_growth/Outputs/Figures/Linear Models/corart_marginal_effects_plot.tiff",
corart.marginal.plot,
dpi = 300, width = 200, height = 150, units = "mm")
White Sucker
Model
#filter data for only Black Crappie, Pomooxis nigromaculatus
catcom <- all.grow.merge %>% filter(species == "common_white_sucker") %>%
filter(age_group %in% c(1, 2, 3, 4, 5), #match age groups in BRTs
!is.na(age_group), !is.na(begin_date_year), !is.na(length_mean_mm),
!is.na(log_max_depth), !is.na(logarea))
#linear model
catcom.lm <- lm(length_mean_mm ~ begin_date_year*age_group + log_max_depth + logarea + doy, data = catcom)
summary(catcom.lm)
Call:
lm(formula = length_mean_mm ~ begin_date_year * age_group + log_max_depth +
logarea + doy, data = catcom)
Residuals:
Min 1Q Median 3Q Max
-136.076 -40.473 -4.099 36.093 194.332
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 954.51748 1253.60238 0.761 0.447
begin_date_year -0.42594 0.63286 -0.673 0.501
age_group2 -137.28866 1483.65129 -0.093 0.926
age_group3 1349.43170 1391.73491 0.970 0.333
age_group4 1052.83341 1406.76845 0.748 0.455
age_group5 367.51727 1477.10904 0.249 0.804
log_max_depth 2.58566 5.18469 0.499 0.618
logarea -0.63291 2.21353 -0.286 0.775
doy 0.35932 0.05293 6.789 5.02e-11 ***
begin_date_year:age_group2 0.12337 0.74995 0.165 0.869
begin_date_year:age_group3 -0.59078 0.70331 -0.840 0.401
begin_date_year:age_group4 -0.41167 0.71095 -0.579 0.563
begin_date_year:age_group5 -0.05380 0.74629 -0.072 0.943
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 55.55 on 342 degrees of freedom
(9 observations deleted due to missingness)
Multiple R-squared: 0.7168, Adjusted R-squared: 0.7068
F-statistic: 72.12 on 12 and 342 DF, p-value: < 2.2e-16#calculate and interpret effect sizes
eta_squared(catcom.lm) begin_date_year age_group log_max_depth logarea
0.0329742424 0.6429364232 0.0009600909 0.0002164386
doy begin_date_year:age_group
0.0376352415 0.0020282284 #interpret(eta_squared(catcom.lm), rules = "cohen1992")
#calculate AIC score
AIC(catcom.lm)[1] 3874.42#examine model fit
testDispersion(catcom.lm)
DHARMa nonparametric dispersion test via sd of residuals fitted vs. simulated
data: simulationOutput
dispersion = 0.96408, p-value = 0.648
alternative hypothesis: two.sided
simulation.output <- simulateResiduals(fittedModel = catcom.lm)
residuals(catcom.lm) 1 2 3 4 5 6 7 8
36.39780836 -47.34506862 -94.17823671 -78.72749364 -6.78219164 -106.28514959 -96.45173528 -81.98623671
9 10 11 12 13 14 15 16
-60.31249364 -34.72219164 -121.52514959 -48.19173528 -67.08490337 -83.17249364 -2.47173528 55.88706661
17 18 19 20 21 22 23 24
99.83103320 91.23251225 81.40651849 26.37226156 -10.27192940 -25.51192940 -40.54079825 -84.39374758
25 26 27 28 29 30 31 32
-4.61176416 -18.58268776 28.24687655 16.31801178 16.57802986 2.06533809 -30.95466191 -27.78562458
33 34 35 36 37 38 39 40
-13.17466191 -48.64734118 57.94533809 -81.29886990 -50.38345560 -40.40529036 -20.50954728 -74.27195702
41 42 43 44 45 46 47 48
-40.98545560 -72.68445702 -78.92954728 -57.16886990 -57.49545560 -12.04195702 -22.12221913 -20.38080067
49 52 53 54 55 56 57 58
-44.51350258 57.64371815 11.34709560 22.44248231 27.11284626 3.67727111 54.76083622 54.68081124
59 60 61 62 63 64 65 66
-22.28799975 -42.52608285 -13.33808900 -32.34107849 30.90807688 57.10414079 50.55302689 12.29944961
67 68 69 70 71 72 73 74
-4.36789844 -33.92541880 -24.27406694 -43.79800303 -30.77613326 32.04865579 32.44943021 102.09334496
75 76 77 78 79 80 81 82
7.78014198 9.81631090 -4.04541770 -0.46885466 -11.32480032 23.77058639 -55.02274161 98.11552979
83 84 85 86 87 88 89 90
65.17150095 -7.25193601 -76.10788167 -104.01249496 42.54782711 21.69188145 35.78726816 157.39770527
91 92 93 94 95 96 97 98
108.34353327 97.10312636 53.53213131 133.01529340 25.25820624 -25.60352237 25.97304068 5.11709502
99 100 101 102 103 104 105 106
26.21248173 8.96964167 -105.89208693 -47.31552388 -44.17146954 -11.68847717 73.94338572 3.92476331
107 108 109 110 111 112 113 114
4.95375399 136.24456731 65.86553815 -5.26109054 27.04607317 32.15280426 121.19857823 -28.32512101
115 116 117 118 119 120 121 122
-50.12535913 -83.57382891 -76.82897184 -43.60313301 -61.37130135 -51.88060544 -32.17033194 -68.40554289
123 124 125 126 127 128 129 130
-37.37239181 -19.74476847 24.49914628 -66.62003617 194.33168062 1.25924795 -14.61298216 24.21426737
131 132 133 134 135 136 137 138
0.06220349 -8.51391039 38.69817420 63.37274900 11.65310970 133.33114282 114.32411966 88.56024765
139 140 141 142 143 144 145 146
78.23925172 136.11330484 -39.56943503 29.90034331 58.88038490 -24.65834004 9.54798710 -136.07628073
147 148 149 150 151 152 153 154
-70.36375658 -25.19559694 35.63013821 57.37192095 -82.24486171 -44.29723837 30.42667638 74.68162289
155 156 157 158 159 160 161 165
54.76239731 65.98631206 -82.41276703 73.26291051 -55.51596369 -54.09418095 51.94819653 -7.82323365
166 167 168 169 170 171 172 173
-22.85739481 29.72225592 28.53732483 34.41338874 20.24227483 18.66141735 56.23072339 49.40678731
174 175 176 177 178 179 180 181
1.70408777 2.77297386 -3.88788362 -33.47668707 -51.63437453 -21.78660463 -48.29950303 -79.64462454
182 183 184 185 186 187 188 189
14.00349119 -21.93419627 -22.56642637 24.58067523 21.17555372 -42.03795085 -27.17563831 -43.04786841
190 191 192 193 194 195 196 197
-23.84076681 -14.54588832 -26.53756806 -26.91525552 -50.40748562 1.81961598 -16.82550552 -20.47442385
198 199 200 201 202 203 204 205
-33.67548189 -69.15356499 -32.34557114 -25.94856063 40.84286036 -20.45747575 2.67661981 42.88815004
206 207 208 209 210 211 212 213
39.47300710 -26.36115406 -23.95014434 89.92138589 30.83208179 96.36297655 44.29027463 41.41879022
214 215 216 217 218 219 220 221
53.84703232 4.54170961 42.79730171 6.54760180 14.15791258 9.93490539 23.07317679 -20.35026016
222 223 224 225 226 227 228 229
-48.20620582 -41.11081912 67.35632611 30.95964715 58.42206319 84.56522528 28.56784476 59.11908372
230 231 232 233 234 235 236 237
-96.27060822 -10.31308950 -64.69895849 -75.28742827 53.56019235 70.78835199 57.92735606 52.87408715
238 239 240 241 242 243 244 245
-8.88437372 -19.71958467 -24.24643359 -8.09489550 73.81564488 35.16380452 7.06280859 47.72953968
246 247 248 249 250 251 252 257
5.97132242 141.35137435 49.35953399 115.23853806 76.24909758 -20.70899154 -11.27118295 5.85140676
258 259 260 261 262 263 264 265
14.54029286 78.99943538 -39.46099104 -80.49760405 -102.94046390 -47.05951794 -101.59334376 -52.96975601
266 267 268 269 270 271 272 273
-42.80881005 0.36542954 -42.81457046 -49.08263587 171.29855741 -10.76638380 -4.72724128 -39.34453979
274 275 276 277 278 279 280 281
-67.83638015 -50.21737608 61.96570839 67.48083272 -4.07429161 -6.17916728 28.58666073 78.33820077
282 283 284 285 286 287 288 289
96.46637954 20.12238807 49.53824469 38.50016585 37.37120737 59.89972296 63.75578777 75.16372389
290 291 292 293 294 295 296 297
-5.76855147 58.13430099 38.44819732 37.89635696 -28.30463897 68.32563623 14.31042528 9.78357636
298 299 300 301 302 303 304 305
-79.26880031 29.10367012 65.13585025 10.38971934 27.29928568 -33.06416849 -39.29291461 38.64850386
306 307 308 309 310 311 312 313
4.35580195 -21.37568247 -15.58963570 -50.21947636 6.21367472 -4.09870194 17.28521281 -54.10697544
314 315 316 317 318 319 320 321
-44.86892756 -75.90748247 -43.39807463 -48.12989586 -50.58991091 42.83824873 9.65725280 -3.01601611
322 323 324 325 326 327 328 329
18.72576663 -49.52131606 25.66696981 27.05901864 13.82982452 -16.23263675 -37.83413030 -26.32862696
330 331 332 333 334 335 336 337
-23.41867254 -51.71799609 32.50187010 -42.28727405 -26.44484138 -53.43128074 -29.57174449 -47.42617372
338 339 340 341 342 343 344 345
-29.81346790 -120.72067497 -18.76843821 43.20995471 40.40372298 37.76375275 67.53895827 -29.01929363
346 347 348 349 350 351 352 353
-87.08533405 -50.43845646 -1.28867811 -4.07407025 -44.02444667 41.28660620 -119.89950021 -24.09814730
354 355 356 357 358 359 360 361
33.43041218 39.46955470 -31.70598893 -11.89451258 -50.40722573 -62.03783612 -32.73254927 -10.01845623
362 363 364
-29.00554218 -0.72292191 -74.90840671 residuals(catcom.lm, quantileFunction = qnorm) 1 2 3 4 5 6 7 8
36.39780836 -47.34506862 -94.17823671 -78.72749364 -6.78219164 -106.28514959 -96.45173528 -81.98623671
9 10 11 12 13 14 15 16
-60.31249364 -34.72219164 -121.52514959 -48.19173528 -67.08490337 -83.17249364 -2.47173528 55.88706661
17 18 19 20 21 22 23 24
99.83103320 91.23251225 81.40651849 26.37226156 -10.27192940 -25.51192940 -40.54079825 -84.39374758
25 26 27 28 29 30 31 32
-4.61176416 -18.58268776 28.24687655 16.31801178 16.57802986 2.06533809 -30.95466191 -27.78562458
33 34 35 36 37 38 39 40
-13.17466191 -48.64734118 57.94533809 -81.29886990 -50.38345560 -40.40529036 -20.50954728 -74.27195702
41 42 43 44 45 46 47 48
-40.98545560 -72.68445702 -78.92954728 -57.16886990 -57.49545560 -12.04195702 -22.12221913 -20.38080067
49 52 53 54 55 56 57 58
-44.51350258 57.64371815 11.34709560 22.44248231 27.11284626 3.67727111 54.76083622 54.68081124
59 60 61 62 63 64 65 66
-22.28799975 -42.52608285 -13.33808900 -32.34107849 30.90807688 57.10414079 50.55302689 12.29944961
67 68 69 70 71 72 73 74
-4.36789844 -33.92541880 -24.27406694 -43.79800303 -30.77613326 32.04865579 32.44943021 102.09334496
75 76 77 78 79 80 81 82
7.78014198 9.81631090 -4.04541770 -0.46885466 -11.32480032 23.77058639 -55.02274161 98.11552979
83 84 85 86 87 88 89 90
65.17150095 -7.25193601 -76.10788167 -104.01249496 42.54782711 21.69188145 35.78726816 157.39770527
91 92 93 94 95 96 97 98
108.34353327 97.10312636 53.53213131 133.01529340 25.25820624 -25.60352237 25.97304068 5.11709502
99 100 101 102 103 104 105 106
26.21248173 8.96964167 -105.89208693 -47.31552388 -44.17146954 -11.68847717 73.94338572 3.92476331
107 108 109 110 111 112 113 114
4.95375399 136.24456731 65.86553815 -5.26109054 27.04607317 32.15280426 121.19857823 -28.32512101
115 116 117 118 119 120 121 122
-50.12535913 -83.57382891 -76.82897184 -43.60313301 -61.37130135 -51.88060544 -32.17033194 -68.40554289
123 124 125 126 127 128 129 130
-37.37239181 -19.74476847 24.49914628 -66.62003617 194.33168062 1.25924795 -14.61298216 24.21426737
131 132 133 134 135 136 137 138
0.06220349 -8.51391039 38.69817420 63.37274900 11.65310970 133.33114282 114.32411966 88.56024765
139 140 141 142 143 144 145 146
78.23925172 136.11330484 -39.56943503 29.90034331 58.88038490 -24.65834004 9.54798710 -136.07628073
147 148 149 150 151 152 153 154
-70.36375658 -25.19559694 35.63013821 57.37192095 -82.24486171 -44.29723837 30.42667638 74.68162289
155 156 157 158 159 160 161 165
54.76239731 65.98631206 -82.41276703 73.26291051 -55.51596369 -54.09418095 51.94819653 -7.82323365
166 167 168 169 170 171 172 173
-22.85739481 29.72225592 28.53732483 34.41338874 20.24227483 18.66141735 56.23072339 49.40678731
174 175 176 177 178 179 180 181
1.70408777 2.77297386 -3.88788362 -33.47668707 -51.63437453 -21.78660463 -48.29950303 -79.64462454
182 183 184 185 186 187 188 189
14.00349119 -21.93419627 -22.56642637 24.58067523 21.17555372 -42.03795085 -27.17563831 -43.04786841
190 191 192 193 194 195 196 197
-23.84076681 -14.54588832 -26.53756806 -26.91525552 -50.40748562 1.81961598 -16.82550552 -20.47442385
198 199 200 201 202 203 204 205
-33.67548189 -69.15356499 -32.34557114 -25.94856063 40.84286036 -20.45747575 2.67661981 42.88815004
206 207 208 209 210 211 212 213
39.47300710 -26.36115406 -23.95014434 89.92138589 30.83208179 96.36297655 44.29027463 41.41879022
214 215 216 217 218 219 220 221
53.84703232 4.54170961 42.79730171 6.54760180 14.15791258 9.93490539 23.07317679 -20.35026016
222 223 224 225 226 227 228 229
-48.20620582 -41.11081912 67.35632611 30.95964715 58.42206319 84.56522528 28.56784476 59.11908372
230 231 232 233 234 235 236 237
-96.27060822 -10.31308950 -64.69895849 -75.28742827 53.56019235 70.78835199 57.92735606 52.87408715
238 239 240 241 242 243 244 245
-8.88437372 -19.71958467 -24.24643359 -8.09489550 73.81564488 35.16380452 7.06280859 47.72953968
246 247 248 249 250 251 252 257
5.97132242 141.35137435 49.35953399 115.23853806 76.24909758 -20.70899154 -11.27118295 5.85140676
258 259 260 261 262 263 264 265
14.54029286 78.99943538 -39.46099104 -80.49760405 -102.94046390 -47.05951794 -101.59334376 -52.96975601
266 267 268 269 270 271 272 273
-42.80881005 0.36542954 -42.81457046 -49.08263587 171.29855741 -10.76638380 -4.72724128 -39.34453979
274 275 276 277 278 279 280 281
-67.83638015 -50.21737608 61.96570839 67.48083272 -4.07429161 -6.17916728 28.58666073 78.33820077
282 283 284 285 286 287 288 289
96.46637954 20.12238807 49.53824469 38.50016585 37.37120737 59.89972296 63.75578777 75.16372389
290 291 292 293 294 295 296 297
-5.76855147 58.13430099 38.44819732 37.89635696 -28.30463897 68.32563623 14.31042528 9.78357636
298 299 300 301 302 303 304 305
-79.26880031 29.10367012 65.13585025 10.38971934 27.29928568 -33.06416849 -39.29291461 38.64850386
306 307 308 309 310 311 312 313
4.35580195 -21.37568247 -15.58963570 -50.21947636 6.21367472 -4.09870194 17.28521281 -54.10697544
314 315 316 317 318 319 320 321
-44.86892756 -75.90748247 -43.39807463 -48.12989586 -50.58991091 42.83824873 9.65725280 -3.01601611
322 323 324 325 326 327 328 329
18.72576663 -49.52131606 25.66696981 27.05901864 13.82982452 -16.23263675 -37.83413030 -26.32862696
330 331 332 333 334 335 336 337
-23.41867254 -51.71799609 32.50187010 -42.28727405 -26.44484138 -53.43128074 -29.57174449 -47.42617372
338 339 340 341 342 343 344 345
-29.81346790 -120.72067497 -18.76843821 43.20995471 40.40372298 37.76375275 67.53895827 -29.01929363
346 347 348 349 350 351 352 353
-87.08533405 -50.43845646 -1.28867811 -4.07407025 -44.02444667 41.28660620 -119.89950021 -24.09814730
354 355 356 357 358 359 360 361
33.43041218 39.46955470 -31.70598893 -11.89451258 -50.40722573 -62.03783612 -32.73254927 -10.01845623
362 363 364
-29.00554218 -0.72292191 -74.90840671 plot(catcom.lm)
Post-hoc comparisons
catcom.emm <- emmeans(catcom.lm, ~ begin_date_year*age_group)
pairs(catcom.emm, simple = "age_group")begin_date_year = 1979:
contrast estimate SE df t.ratio p.value
age_group1 - age_group2 -106.8 10.50 342 -10.192 <.0001
age_group1 - age_group3 -180.5 10.10 342 -17.827 <.0001
age_group1 - age_group4 -238.3 10.30 342 -23.125 <.0001
age_group1 - age_group5 -261.1 11.10 342 -23.524 <.0001
age_group2 - age_group3 -73.7 8.61 342 -8.551 <.0001
age_group2 - age_group4 -131.5 8.84 342 -14.864 <.0001
age_group2 - age_group5 -154.2 9.77 342 -15.781 <.0001
age_group3 - age_group4 -57.8 8.40 342 -6.882 <.0001
age_group3 - age_group5 -80.6 9.39 342 -8.584 <.0001
age_group4 - age_group5 -22.8 9.56 342 -2.384 0.1221
P value adjustment: tukey method for comparing a family of 5 estimates test(pairs(catcom.emm, by = "begin_date_year"), by = NULL, adjust = "mvt") contrast begin_date_year estimate SE df t.ratio p.value
age_group1 - age_group2 1979 -106.8 10.50 342 -10.192 <.0001
age_group1 - age_group3 1979 -180.5 10.10 342 -17.827 <.0001
age_group1 - age_group4 1979 -238.3 10.30 342 -23.125 <.0001
age_group1 - age_group5 1979 -261.1 11.10 342 -23.524 <.0001
age_group2 - age_group3 1979 -73.7 8.61 342 -8.551 <.0001
age_group2 - age_group4 1979 -131.5 8.84 342 -14.864 <.0001
age_group2 - age_group5 1979 -154.2 9.77 342 -15.781 <.0001
age_group3 - age_group4 1979 -57.8 8.40 342 -6.882 <.0001
age_group3 - age_group5 1979 -80.6 9.39 342 -8.584 <.0001
age_group4 - age_group5 1979 -22.8 9.56 342 -2.384 0.1209
P value adjustment: mvt method for 10 tests #export tables
# #interpret(eta_squared(catcom.lm), rules = "cohen1992") %>%
# write.csv(file = "Outputs/Tables/catcom_eta_squared.csv")
#slope contrasts
# Obtain slope estimates
catcom.slopes <- emtrends(catcom.lm, ~age_group, var = "begin_date_year")
# Compare estimated slopes to 0
catcom.slope.contrasts <- test(catcom.slopes) %>%
mutate(Species = "White Sucker") %>%
rename(Age = age_group)
catcom.slope.contrasts %>%
write.csv(file = "Outputs/Tables/catcom_emmeans.csv")Plot raw data
(catcom.length.year.plot <- ggplot(data = catcom %>%
mutate(Age = as.factor(age_group)),
aes(x = begin_date_year, y = length_mean_mm,
color = Age, fill = Age, group = Age))+
#geom_point()+
geom_smooth(method = "lm")+
stat_poly_eq(use_label("eq"))+
scale_color_viridis_d()+
scale_fill_viridis_d()+
labs(y = "Length (mm)", x = "Year")+
theme_bw()
)`geom_smooth()` using formula = 'y ~ x'
Marginal Effects
result <- predict_response(catcom.lm, c("begin_date_year", "age_group"))
# plot(result)
# test_predictions(result) %>%
# write.csv(file = "Outputs/Tables/catcom_pairwise_length_time_slopes.csv", row.names = F)
(catcom.marginal.plot <- ggpredict(catcom.lm, terms = c("begin_date_year", "age_group")) %>%
plot()+
#stat_poly_eq(use_label("eq"))+
stat_regline_equation()+
stat_poly_line()+
# annotate(geom = "text", label = "y = 2400 - 0.99x", x = 2000, y = 450)+
# annotate(geom = "text", label = "y = 4300 - 2x", x = 2000, y = 400)+
# annotate(geom = "text", label = "y = 4300 - 2x", x = 2000, y = 350)+
# annotate(geom = "text", label = "y = 3700 - 1.7x", x = 2000, y = 300)+
# annotate(geom = "text", label = "y = 2600 - 1.2x", x = 2000, y = 200)+
scale_color_viridis_d()+
scale_fill_viridis_d()+
labs(title = NULL,
y = "Mean Length-at-Age (mm)",
x = "Year")+
guides(fill = guide_legend(title = "Age", reverse = T), color = guide_legend(title = "Age", reverse = T))+
theme_bw()
)Scale for colour is already present.
Adding another scale for colour, which will replace the existing scale.ggsave(filename = "~/GitHub/EAGER_growth/Outputs/Figures/Linear Models/catcom_marginal_effects_plot.tiff",
catcom.marginal.plot,
dpi = 300, width = 200, height = 150, units = "mm")
Largemouth Bass
Model
#filter data for only Black Crappie, Pomooxis nigromaculatus
micsal <- all.grow.merge %>% filter(species == "largemouth_bass") %>%
filter(!age_group %in% c(13, 14, 15, 16, 17, 18, 19, 20, 21, 22), #match age groups in BRTs
!is.na(age_group), !is.na(begin_date_year), !is.na(length_mean_mm),
!is.na(log_max_depth), !is.na(logarea))
#linear model
micsal.lm <- lm(length_mean_mm ~ begin_date_year*age_group + log_max_depth + logarea + doy, data = micsal)
summary(micsal.lm)
Call:
lm(formula = length_mean_mm ~ begin_date_year * age_group + log_max_depth +
logarea + doy, data = micsal)
Residuals:
Min 1Q Median 3Q Max
-208.506 -23.871 -0.299 22.941 279.766
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 1163.65056 323.33490 3.599 0.000321 ***
begin_date_year -0.57105 0.16302 -3.503 0.000462 ***
age_group1 523.22747 351.71109 1.488 0.136873
age_group2 25.36943 342.49966 0.074 0.940955
age_group3 -220.91353 340.51620 -0.649 0.516509
age_group4 -375.25423 344.59940 -1.089 0.276200
age_group5 -225.09329 351.34976 -0.641 0.521764
age_group6 246.59435 360.32332 0.684 0.493759
age_group7 778.71599 373.32510 2.086 0.037015 *
age_group8 857.37930 397.10066 2.159 0.030868 *
age_group9 892.42675 428.32799 2.084 0.037231 *
age_group10 501.54486 469.29233 1.069 0.285220
age_group11 158.22737 588.50719 0.269 0.788042
age_group12 470.82810 727.77331 0.647 0.517684
log_max_depth -4.93491 0.61810 -7.984 1.58e-15 ***
logarea 4.67034 0.32431 14.401 < 2e-16 ***
doy 0.18877 0.00796 23.715 < 2e-16 ***
begin_date_year:age_group1 -0.23044 0.17735 -1.299 0.193866
begin_date_year:age_group2 0.05329 0.17271 0.309 0.757651
begin_date_year:age_group3 0.20504 0.17170 1.194 0.232438
begin_date_year:age_group4 0.30473 0.17372 1.754 0.079443 .
begin_date_year:age_group5 0.24796 0.17707 1.400 0.161436
begin_date_year:age_group6 0.02756 0.18151 0.152 0.879309
begin_date_year:age_group7 -0.22285 0.18797 -1.186 0.235819
begin_date_year:age_group8 -0.24770 0.19975 -1.240 0.214980
begin_date_year:age_group9 -0.25201 0.21531 -1.170 0.241860
begin_date_year:age_group10 -0.04807 0.23572 -0.204 0.838427
begin_date_year:age_group11 0.13175 0.29496 0.447 0.655112
begin_date_year:age_group12 -0.02166 0.36425 -0.059 0.952591
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 37.61 on 9471 degrees of freedom
(83 observations deleted due to missingness)
Multiple R-squared: 0.8681, Adjusted R-squared: 0.8677
F-statistic: 2226 on 28 and 9471 DF, p-value: < 2.2e-16#calculate and interpret effect sizes
eta_squared(micsal.lm) begin_date_year age_group log_max_depth logarea
0.0135893748 0.8422010566 0.0006643162 0.0028253074
doy begin_date_year:age_group
0.0078359130 0.0009653792 #interpret(eta_squared(micsal.lm), rules = "cohen1992")
#calculate AIC score
AIC(micsal.lm)[1] 95910.58#examine model fit
testDispersion(micsal.lm)
DHARMa nonparametric dispersion test via sd of residuals fitted vs. simulated
data: simulationOutput
dispersion = 0.99711, p-value = 0.84
alternative hypothesis: two.sided
simulation.output <- simulateResiduals(fittedModel = micsal.lm)
residuals(micsal.lm) 1 2 3 4 5 6 7 8
38.93612888 9.20776915 -14.96687046 -37.85977549 -36.76913522 54.60893178 -26.10210871 10.54031971
9 10 11 12 13 14 15 16
6.37846240 -7.31253535 -4.96033152 -3.01429604 3.94751852 -2.22769917 38.26429975 -26.50664027
17 18 19 20 21 22 23 24
2.64120552 -7.98832502 -20.30726154 -12.08736931 -15.43693848 22.29383677 9.42320559 26.12168682
25 26 27 28 29 30 31 32
-31.53391351 -46.96076642 -53.40044697 -38.13686857 -68.92921581 -31.80627381 1.26451449 -16.32803455
33 34 35 36 37 38 39 40
-11.87188997 -20.40278738 -18.01940219 24.85925059 -20.83717101 -47.81951825 -43.91968117 -31.94420668
41 42 43 44 45 46 47 48
-59.02847220 -85.62252254 -69.19238438 40.56531653 -14.47511674 -42.53488034 -29.58390777 -23.66115328
49 50 51 52 53 54 55 56
18.78654762 -7.89055231 -37.98231591 -14.88006034 -75.47789050 1.19004909 -4.69672819 -21.63716145
57 58 59 60 61 62 63 64
-70.01692506 -53.39166949 -60.45653133 13.91820882 18.08163862 92.45479404 89.02348775 19.56391606
65 66 67 68 69 70 71 72
3.55374694 -31.07040965 38.03471885 -0.76281224 -20.11140077 -30.34038404 55.24725623 9.02637825
73 74 75 76 77 78 79 80
-26.78438404 -30.17794745 -26.12300048 -22.63214702 3.55570993 -32.75754999 -83.13658336 7.52514488
81 82 83 84 85 86 87 88
25.94699952 18.64285298 9.90570993 -39.10754999 0.85759552 -12.99966715 0.22785298 -6.60429007
89 90 91 92 93 94 95 96
10.42245001 52.25646905 16.09759552 -7.07300048 -75.97214702 -28.94754999 -83.13658336 3.72430162
97 98 99 100 101 102 103 104
-8.65129439 -15.32044092 -15.80258397 16.46415611 84.19512274 7.21685098 1.14817515 15.61928598
105 106 107 108 109 110 111 112
-8.07395305 14.13097481 -81.77149480 -68.86063240 2.02427805 48.23220591 -1.15498533 10.69219807
113 114 115 116 117 118 119 120
-25.86940131 -0.83149085 -43.39656300 53.59652184 -44.37239170 5.81990461 -3.56994531 -14.89041492
121 122 123 124 125 126 127 128
-14.61152714 -43.30513655 -77.17795315 52.24200327 -53.78887347 26.68315530 -1.47629977 26.68315530
129 130 131 132 133 134 135 136
1.06370023 -14.24832108 -3.34442480 -1.87469081 -2.36799673 25.41315530 -5.53981590 41.04385574
137 138 139 140 141 142 143 144
-33.80367735 -8.43251398 17.32018410 -38.33114426 -14.75367735 -37.65338036 -33.70868564 -39.76491402
145 146 147 148 149 150 151 152
-18.23981590 -35.79114426 5.53748602 6.83632265 -27.49338036 -5.76868564 -6.32332287 -9.81322807
153 154 155 156 157 158 159 160
-9.10006787 -13.81142199 -31.71118559 -43.31354635 24.91906998 -33.58079186 12.58123982 6.50394829
161 162 163 164 165 166 167 168
18.35046962 36.14756254 -12.54142199 -52.03118559 24.84312032 -40.75876018 -41.74432206 -19.90742199
169 170 171 172 173 174 175 176
-22.82118559 -24.68687968 -28.42093002 43.88920814 -20.43876018 -16.35605171 -24.48046145 -22.20586052
177 178 179 180 181 182 183 184
-10.88065275 -20.46261416 -17.91354635 -34.77093002 -37.39079186 66.61046962 10.43078439 10.36588251
185 186 187 188 189 190 191 192
36.62955415 -31.70753241 -43.85299011 -23.29378241 -19.12729602 17.61500989 9.56295956 2.30512940
193 194 195 196 197 198 199 200
-40.18564080 -21.11854788 -16.72153241 8.81270398 -24.80299011 -1.86704044 -19.09190228 -147.28216213
201 202 203 204 205 206 207 208
-18.57854788 -27.89753241 -20.39729602 -34.88704044 -8.29690228 -9.12487060 -26.19854788 -29.67657187
209 210 211 212 213 214 215 216
-2.49043248 -29.59086574 -6.42729602 -54.01299011 -53.61034728 -28.43883097 1.24955422 -15.24429300
217 218 219 220 221 222 223 224
-11.34721460 2.14110483 -54.29261984 3.10424154 20.63320700 -48.23556184 -38.41740239 -11.95844578
225 226 227 228 229 230 231 232
-39.81879300 -11.09321460 -13.94556184 -13.40575846 -31.61383097 -27.70644578 -28.47345966 -11.09321460
233 234 235 236 237 238 239 240
-31.43261984 -11.92883097 -50.48679300 12.72443816 14.28738016 19.59777143 38.24801650 -5.71396310
241 242 243 244 245 246 247 248
-30.67343757 -53.39204926 -61.80366408 -2.06878013 -23.36523200 -33.17642869 -51.94796102 -61.99243298
249 250 251 252 253 254 255 256
15.42922878 0.36578767 0.64478992 -38.27800625 -40.35363743 -40.16515620 5.25701877 -5.89397898
257 258 259 260 261 262 263 264
-63.40240633 -65.15580947 -44.87352046 -12.04164236 27.92824987 14.68445595 12.60882477 -25.36285707
265 266 267 268 269 270 271 272
-9.10679358 -9.83462862 -0.39623243 -22.38569527 -21.28632645 -33.16284523 -48.06020667 9.34527041
273 274 275 276 277 278 279 280
9.30755942 -1.00825215 -11.44693737 26.86093920 22.31163199 35.99607533 42.43913259 -23.18368401
281 282 283 284 285 286 287 288
21.08209370 39.95478665 -18.52131165 103.99858057 30.33858057 53.11915547 65.81915547 14.28027526
289 290 291 292 293 294 295 296
-33.18759343 -16.48702995 11.31202895 33.60436453 32.78156836 48.48593718 18.27323574 13.52688852
297 298 299 300 301 302 303 304
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889 890 891 892 893 894 895 896
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905 906 907 908 909 910 911 912
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913 914 915 916 917 918 919 920
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929 930 931 932 933 934 935 936
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945 946 947 948 949 950 951 952
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953 954 955 956 957 958 959 960
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961 962 963 964 965 966 967 968
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977 978 979 980 981 982 983 984
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985 986 987 988 989 990 991 992
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993 994 995 996 997 998 999 1000
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[ reached 'max' / getOption("max.print") -- omitted 8500 entries ]residuals(micsal.lm, quantileFunction = qnorm) 1 2 3 4 5 6 7 8
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9 10 11 12 13 14 15 16
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17 18 19 20 21 22 23 24
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25 26 27 28 29 30 31 32
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33 34 35 36 37 38 39 40
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41 42 43 44 45 46 47 48
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49 50 51 52 53 54 55 56
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57 58 59 60 61 62 63 64
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65 66 67 68 69 70 71 72
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73 74 75 76 77 78 79 80
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225 226 227 228 229 230 231 232
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-8.38061148 5.08380025 30.04106168 -14.16207694 -10.15588568 9.77184998 -30.90388224 -35.90426292
313 314 315 316 317 318 319 320
-45.50350805 33.56096089 26.52852870 49.60990766 -15.66388224 -56.22426292 -34.07350805 6.89972161
321 322 323 324 325 326 327 328
-11.85388224 -31.67092958 -26.45350805 -33.16834881 -38.43670399 27.21972161 -23.28388224 -32.34826292
329 330 331 332 333 334 335 336
-55.66350805 2.11611776 -35.90426292 -64.55350805 -35.70834881 9.82329601 -8.81009234 -8.15387983
337 338 339 340 341 342 343 344
5.55589477 -22.63868555 51.17463619 -56.92868555 -35.08410523 33.36669731 -22.38410523 38.32131445
345 346 347 348 349 350 351 352
-30.10536381 57.49669731 -11.34340092 -93.50410523 -47.61535222 54.98463619 66.38669731 29.29659908
353 354 355 356 357 358 359 360
-1.40046471 -13.03044249 -40.77046471 49.40022130 13.53061744 -2.41564800 -26.19693187 -34.68389407
361 362 363 364 365 366 367 368
-21.36792443 -120.12506102 -3.02613314 16.05221339 -1.60044249 -0.31120727 -18.30404923 -70.64693187
369 370 371 372 373 374 375 376
-13.03259968 48.82298274 58.63013186 52.16686416 49.17997551 23.41742753 20.62924546 59.82964941
377 378 379 380 381 382 383 384
0.57444246 -2.44926635 3.94964941 -13.96291279 -12.82437571 -68.13639426 -20.54893601 28.83661844
385 386 387 388 389 390 391 392
-41.34365764 24.51441129 -11.95927394 -22.06237638 -30.41181569 -3.03959457 19.48397942 59.12238004
393 394 395 396 397 398 399 400
-10.45470951 4.95941541 38.25830319 21.41802712 18.53321052 -0.03046571 -9.57968169 -17.66981127
401 402 403 404 405 406 407 408
17.36229508 18.48784953 62.60757346 -89.87104658 -12.65562691 109.41769484 -5.25841107 90.24640563
409 410 411 412 413 414 415 416
-21.16487823 -10.60184043 -31.92841107 -44.62841107 -54.98838885 67.38640563 -8.46487823 -16.18355280
417 418 419 420 421 422 423 424
-34.53140861 -33.49003722 -8.27878514 7.89997889 -5.49435642 -12.37563881 -13.38585389 4.66049320
425 426 427 428 429 430 431 432
-30.34295113 26.39780720 7.33572726 11.02798195 -12.95622223 28.30172460 60.09400732 53.47213564
433 434 435 436 437 438 439 440
30.92792258 6.52151862 -22.73527763 21.43603715 -0.06450461 -13.67095015 -12.73122623 -37.44564221
441 442 443 444 445 446 447 448
-59.51848138 1.92388904 11.48770381 11.25596500 10.77282873 -6.05095015 34.87645343 -31.32239453
449 450 451 452 453 454 455 456
1.47815039 9.02612909 23.46300905 -9.22076817 -24.79699095 24.71645343 -0.84239453 -12.91518294
457 458 459 460 461 462 463 464
9.19423183 3.77557670 33.57946242 -30.12595493 5.24312010 16.93760547 3.38315039 -6.68076817
465 466 467 468 469 470 471 472
-41.94442330 53.94300905 3.60232331 -19.59169720 -40.60454307 -41.48584835 -91.75839078 -16.11523058
473 474 475 476 477 478 479 480
30.18383229 -27.73007673 6.49502139 -22.55686252 -37.03767669 -51.23284005 37.88145693 -23.12130697
481 482 483 484 485 486 487 488
-40.42434336 -37.00884005 -81.49854307 -35.00884835 -16.11523058 -24.33767669 -26.21384005 -56.09854307
489 490 491 492 493 494 495 496
-67.39384835 -91.75839078 -24.17407673 9.03502139 -35.82130697 -59.89767669 -57.62254307 30.18383229
497 498 499 500 501 502 503 504
0.76301898 60.98980727 9.00356051 27.13597235 -19.95491516 17.25856051 5.84301898 -24.50866009
505 506 507 508 509 510 511 512
33.55780727 17.53070337 27.77097235 9.90805995 -44.95698102 82.32396347 -24.27656711 18.41972409
513 514 515 516 517 518 519 520
-36.14136016 -58.79014541 -13.28136016 -56.29758204 -15.82136016 -28.35758204 -32.16176411 -13.43959285
521 522 523 524 525 526 527 528
-20.36960689 -25.59041128 31.35961094 16.32027473 -16.24732913 25.64461094 -31.21469699 11.46294427
529 530 531 532 533 534 535 536
55.69027473 24.39267087 23.77107209 11.39949269 -17.96942782 -21.54641365 -37.25510774 -36.92515807
537 538 539 540 541 542 543 544
-73.13898823 -18.56018255 22.01791208 -32.59918784 0.04704855 44.15535446 -20.53869588 14.39791208
545 546 547 548 549 550 551 552
-36.40918784 -59.98464554 -73.87869588 -1.56855772 -24.55020332 -19.52386999 -34.41869214 -34.57233053
553 554 555 556 557 558 559 560
25.63290587 31.46316144 17.41329960 28.28803975 42.67456108 -22.82014540 117.56145036 -4.18087562
561 562 563 564 565 566 567 568
-41.92344282 -34.39312336 41.83045503 47.13791117 1.12200470 -12.83080310 -40.96808146 -48.75216686
569 570 571 572 573 574 575 576
-82.65871015 -39.87846509 34.20518231 -0.23266197 -30.55408146 -41.17204349 10.66045348 3.19821082
577 578 579 580 581 582 583 584
-11.18804320 -30.87981541 -24.47241364 33.88738134 4.49188205 27.25062347 -28.52811795 1.35783801
585 586 587 588 589 590 591 592
-9.95719441 -16.15248301 -8.53248301 13.44039552 -5.00027888 -17.48356583 -26.07613797 -15.61610758
593 594 595 596 597 598 599 600
-26.17455313 -10.97478193 -3.15786809 -9.53808435 -23.97821394 -70.82356583 -32.74363797 22.61692168
601 602 603 604 605 606 607 608
17.93178606 -18.75356583 -34.64863797 -39.74610758 -33.96388646 -0.85082920 14.62213191 -4.92821394
609 610 611 612 613 614 615 616
-20.65856583 -45.44363797 -47.78944091 -37.35055313 -21.17082920 1.72521807 18.59627518 9.90693898
617 618 619 620 621 622 623 624
-6.65866489 -24.63693033 -11.58821419 -49.48755502 -35.35400732 -17.01134894 -26.98920235 10.31150110
625 626 627 628 629 630 631 632
-16.43919367 -43.51738066 -59.61852438 -37.70927011 -24.20597774 -23.97182348 20.20746675 3.01858811
633 634 635 636 637 638 639 640
-50.99278733 -30.30077666 -50.08470600 -62.84985253 -68.49666225 -79.21639835 -38.94162221 -41.20156063
641 642 643 644 645 646 647 648
-54.67856980 6.37085212 -28.35412563 -15.00213090 -12.38130436 4.78047471 89.98199155 14.23744747
649 650 651 652 653 654 655 656
3.62453577 -3.57597103 19.17380804 51.88199155 -22.51336517 -22.80355947 -3.99160739 15.36380804
657 658 659 660 661 662 663 664
55.26865822 51.78991727 42.09839968 -16.41598383 8.52167213 -27.51093014 -18.19759681 -18.87660883
665 666 667 668 669 670 671 672
8.64867213 7.46783412 2.59746660 -5.55539811 21.78598275 -1.82744073 -13.19297962 -9.58155863
673 674 675 676 677 678 679 680
-30.20385759 13.80661121 -8.30174852 -13.32055480 -26.58262650 2.95194852 29.60043438 4.86522889
681 682 683 684 685 686 687 688
3.07686916 -4.16443712 37.77283102 -1.34415577 -16.66887017 42.54063639 25.06016885 -15.56716898
689 690 691 692 693 694 695 696
1.90063639 -85.11835204 -21.24353208 -23.99649110 -34.81345075 14.39740272 -0.81674033 -56.68800025
697 698 699 700 701 702 703 704
-8.00703362 22.64714526 -0.52345075 -24.26704173 -49.58474033 5.54199975 73.27296638 -3.06345075
705 706 707 708 709 710 711 712
-16.08259728 1.21525967 -8.00703362 77.57469462 63.88601879 -23.38345075 -5.92259728 6.29525967
713 714 715 716 717 718 719 720
-12.23800025 -31.61017498 -55.13753086 11.26010798 -36.45561054 -31.61963309 -78.95232041 -19.31252364
721 722 723 724 725 726 727 728
9.07562055 5.44493134 4.67891069 -38.28784756 -25.49051510 -35.59314014 30.25091922 -32.12661646
729 730 731 732 733 734 735 736
-4.91113981 20.66817097 45.30215032 1.69153516 -29.31764441 -37.33710724 -77.39178604 -62.50759053
737 738 739 740 741 742 743 744
-74.01828531 -97.46947490 -8.19541556 -15.34320948 -55.26484066 -66.50635943 -21.71372088 -7.03066945
745 746 747 748 749 750 751 752
-17.47971373 -38.79071148 12.45649235 -19.56801905 -27.42686345 -39.01236604 -55.76052947 -54.26943449
753 754 755 756 757 758 759 760
-52.96712755 -40.32906056 -52.71127028 -57.17977095 -53.28149130 -56.70716868 -32.31216053 -11.83137421
761 762 763 764 765 766 767 768
-24.74673892 -21.42952472 -57.74294820 12.73135048 -0.56119863 -35.52130640 -53.02230415 -51.30510032
769 770 771 772 773 774 775 776
-49.02644579 -35.95653599 -54.11961171 -46.06270607 -35.98073451 1.44453096 -36.92523434 -33.00217545
777 778 779 780 781 782 783 784
-28.59567319 -28.32989794 -48.91124340 -18.60561932 -28.14846937 -54.16561932 -12.54123434 -26.96967545
785 786 787 788 789 790 791 792
-32.52112774 -32.17013603 -35.21943388 -21.68971185 -62.95754937 -43.06146303 -20.39983359 -10.59341350
793 794 795 796 797 798 799 800
-30.75017840 4.69410209 -5.72788342 2.73707020 11.69750566 22.13869828 -12.81206480 -12.93788686
801 802 803 804 805 806 807 808
-10.84459889 -14.71539506 2.51297376 33.43545498 53.08357062 -24.95776379 -26.44302624 -41.76023735
809 810 811 812 813 814 815 816
-12.59250719 8.75071837 -15.10027938 -4.02870673 -14.97497510 -25.05574827 -49.28858055 -22.36556734
817 818 819 820 821 822 823 824
-24.28830843 -8.54574827 1.51141945 -4.58556734 8.73169157 -13.62574827 -22.93608055 -11.42402888
825 826 827 828 829 830 831 832
-11.02028175 -15.31077518 5.42425173 -13.09358055 -27.44556734 -85.95028175 -25.66492098 -25.82153580
833 834 835 836 837 838 839 840
-37.04488301 -24.19430462 -31.19870986 -26.20920669 -69.63653580 84.39069538 -60.70165185 31.27815152
841 842 843 844 845 846 847 848
16.40832438 -29.66368988 -55.69975260 -62.98609981 26.89513135 11.29510758 6.13445504 -16.09276725
849 850 851 852 853 854 855 856
-38.80123687 -54.57433803 -53.52966730 -53.15374564 -5.31853579 -3.73074551 19.38042372 -24.09524030
857 858 859 860 861 862 863 864
-40.06916964 -42.08164951 -42.35212589 -53.47705248 -47.12941918 -98.70769094 -46.35636678 -14.59772929
865 866 867 868 869 870 871 872
29.07474406 -96.32712589 -9.75641764 -27.36095459 -33.29583631 -62.82498284 -39.60045923 -47.55038581
873 874 875 876 877 878 879 880
-26.80941918 -47.90769094 37.97776648 5.01281833 -19.31269555 1.98338284 -22.81898816 -10.28223352
881 882 883 884 885 886 887 888
-4.72384834 -8.20019555 -5.63661716 -1.79523410 9.39103554 5.76089207 9.33408427 -11.22552742
889 890 891 892 893 894 895 896
-7.20928510 4.19151055 -11.02491106 -73.27431630 -4.68441734 -27.26541509 1.75678873 11.74615755
897 898 899 900 901 902 903 904
-49.73495657 -12.30441734 25.00692152 -18.20818625 11.91581600 8.12968650 -53.02413012 32.43815262
905 906 907 908 909 910 911 912
-20.30195515 -63.20295290 -29.05611779 -38.02394961 7.44927595 28.89494487 -10.23951797 4.32185085
913 914 915 916 917 918 919 920
-29.69500126 -31.46902937 5.57873672 9.38938372 -25.57072405 22.27990384 -5.32050289 -26.80864402
921 922 923 924 925 926 927 928
-29.15081127 -53.82868871 -25.51655108 -47.92963935 -44.43198659 -16.70704459 -46.43218321 -34.63201036
929 930 931 932 933 934 935 936
-9.96836363 -31.62583809 -44.94644979 -31.43441182 -5.58750009 -16.90651399 -11.61823866 -12.41559950
937 938 939 940 941 942 943 944
-11.76320443 -8.12718602 7.92366647 -17.51805908 -11.67910564 -1.76502101 17.97934022 32.14119972
945 946 947 948 949 950 951 952
40.03006598 -12.00926781 35.29221129 17.36325533 -25.43377738 -30.77018190 -2.44920142 15.04118612
953 954 955 956 957 958 959 960
7.90166120 -34.90305932 37.74919137 -50.25211769 -52.23862921 -5.18517763 1.36548617 2.88961686
961 962 963 964 965 966 967 968
-12.00166700 -16.74252175 -46.23979616 -14.69211769 -8.54038314 -72.96166700 -27.49265956 -89.41979616
969 970 971 972 973 974 975 976
-70.53420160 9.23252368 -26.87419667 -35.21054072 -45.87404539 -42.33217482 -24.21976480 -10.77023010
977 978 979 980 981 982 983 984
-39.37898691 -31.05002156 -36.64537872 -38.34074625 15.73638904 3.70776990 -7.43537872 -23.63361096
985 986 987 988 989 990 991 992
-8.19002156 -89.67361096 13.86776990 -7.20565357 -19.81532599 28.72805532 -4.45079265 -20.60297500
993 994 995 996 997 998 999 1000
-40.76916629 -4.91282142 52.85805532 10.78920735 -0.60624772 -6.73316629 -6.18282142 -22.52226903
[ reached 'max' / getOption("max.print") -- omitted 8500 entries ]plot(micsal.lm)
Post-hoc comparisons
micsal.emm <- emmeans(micsal.lm, ~ begin_date_year*age_group)
pairs(micsal.emm, simple = "age_group")begin_date_year = 1992:
contrast estimate SE df t.ratio p.value
age_group0 - age_group1 -64.28 3.25 9471 -19.805 <.0001
age_group0 - age_group2 -131.51 3.15 9471 -41.717 <.0001
age_group0 - age_group3 -187.45 3.13 9471 -59.826 <.0001
age_group0 - age_group4 -231.66 3.16 9471 -73.419 <.0001
age_group0 - age_group5 -268.75 3.19 9471 -84.193 <.0001
age_group0 - age_group6 -301.49 3.27 9471 -92.326 <.0001
age_group0 - age_group7 -334.87 3.38 9471 -99.041 <.0001
age_group0 - age_group8 -364.05 3.57 9471 -102.017 <.0001
age_group0 - age_group9 -390.52 3.78 9471 -103.374 <.0001
age_group0 - age_group10 -405.81 4.21 9471 -96.316 <.0001
age_group0 - age_group11 -420.63 5.15 9471 -81.730 <.0001
age_group0 - age_group12 -427.70 6.38 9471 -67.003 <.0001
age_group1 - age_group2 -67.23 1.66 9471 -40.457 <.0001
age_group1 - age_group3 -123.18 1.62 9471 -76.172 <.0001
age_group1 - age_group4 -167.39 1.65 9471 -101.198 <.0001
age_group1 - age_group5 -204.47 1.72 9471 -118.868 <.0001
age_group1 - age_group6 -237.21 1.85 9471 -128.376 <.0001
age_group1 - age_group7 -270.60 2.04 9471 -132.463 <.0001
age_group1 - age_group8 -299.77 2.34 9471 -128.046 <.0001
age_group1 - age_group9 -326.24 2.65 9471 -122.952 <.0001
age_group1 - age_group10 -341.54 3.24 9471 -105.344 <.0001
age_group1 - age_group11 -356.36 4.39 9471 -81.267 <.0001
age_group1 - age_group12 -363.42 5.79 9471 -62.806 <.0001
age_group2 - age_group3 -55.94 1.39 9471 -40.211 <.0001
age_group2 - age_group4 -100.15 1.43 9471 -69.934 <.0001
age_group2 - age_group5 -137.24 1.51 9471 -91.095 <.0001
age_group2 - age_group6 -169.98 1.65 9471 -103.113 <.0001
age_group2 - age_group7 -203.36 1.86 9471 -109.122 <.0001
age_group2 - age_group8 -232.54 2.19 9471 -106.339 <.0001
age_group2 - age_group9 -259.01 2.52 9471 -102.793 <.0001
age_group2 - age_group10 -274.30 3.13 9471 -87.553 <.0001
age_group2 - age_group11 -289.12 4.30 9471 -67.168 <.0001
age_group2 - age_group12 -296.19 5.73 9471 -51.734 <.0001
age_group3 - age_group4 -44.21 1.37 9471 -32.200 <.0001
age_group3 - age_group5 -81.29 1.45 9471 -56.092 <.0001
age_group3 - age_group6 -114.03 1.59 9471 -71.510 <.0001
age_group3 - age_group7 -147.42 1.82 9471 -81.191 <.0001
age_group3 - age_group8 -176.59 2.15 9471 -82.299 <.0001
age_group3 - age_group9 -203.06 2.49 9471 -81.709 <.0001
age_group3 - age_group10 -218.36 3.11 9471 -70.324 <.0001
age_group3 - age_group11 -233.18 4.28 9471 -54.432 <.0001
age_group3 - age_group12 -240.24 5.71 9471 -42.079 <.0001
age_group4 - age_group5 -37.08 1.49 9471 -24.952 <.0001
age_group4 - age_group6 -69.82 1.63 9471 -42.902 <.0001
age_group4 - age_group7 -103.21 1.84 9471 -55.963 <.0001
age_group4 - age_group8 -132.38 2.17 9471 -61.011 <.0001
age_group4 - age_group9 -158.85 2.51 9471 -63.371 <.0001
age_group4 - age_group10 -174.15 3.12 9471 -55.776 <.0001
age_group4 - age_group11 -188.97 4.30 9471 -43.988 <.0001
age_group4 - age_group12 -196.03 5.72 9471 -34.281 <.0001
age_group5 - age_group6 -32.74 1.69 9471 -19.370 <.0001
age_group5 - age_group7 -66.13 1.90 9471 -34.813 <.0001
age_group5 - age_group8 -95.30 2.22 9471 -42.998 <.0001
age_group5 - age_group9 -121.77 2.55 9471 -47.794 <.0001
age_group5 - age_group10 -137.07 3.16 9471 -43.438 <.0001
age_group5 - age_group11 -151.89 4.32 9471 -35.161 <.0001
age_group5 - age_group12 -158.95 5.74 9471 -27.710 <.0001
age_group6 - age_group7 -33.39 2.01 9471 -16.616 <.0001
age_group6 - age_group8 -62.56 2.31 9471 -27.076 <.0001
age_group6 - age_group9 -89.03 2.63 9471 -33.838 <.0001
age_group6 - age_group10 -104.33 3.22 9471 -32.369 <.0001
age_group6 - age_group11 -119.15 4.37 9471 -27.272 <.0001
age_group6 - age_group12 -126.21 5.77 9471 -21.861 <.0001
age_group7 - age_group8 -29.17 2.47 9471 -11.826 <.0001
age_group7 - age_group9 -55.64 2.77 9471 -20.088 <.0001
age_group7 - age_group10 -70.94 3.34 9471 -21.256 <.0001
age_group7 - age_group11 -85.76 4.45 9471 -19.256 <.0001
age_group7 - age_group12 -92.82 5.84 9471 -15.901 <.0001
age_group8 - age_group9 -26.47 3.00 9471 -8.837 <.0001
age_group8 - age_group10 -41.77 3.53 9471 -11.841 <.0001
age_group8 - age_group11 -56.59 4.60 9471 -12.310 <.0001
age_group8 - age_group12 -63.65 5.95 9471 -10.701 <.0001
age_group9 - age_group10 -15.30 3.74 9471 -4.085 0.0030
age_group9 - age_group11 -30.12 4.77 9471 -6.319 <.0001
age_group9 - age_group12 -37.18 6.08 9471 -6.115 <.0001
age_group10 - age_group11 -14.82 5.12 9471 -2.896 0.1606
age_group10 - age_group12 -21.88 6.36 9471 -3.442 0.0332
age_group11 - age_group12 -7.06 7.01 9471 -1.007 0.9987
P value adjustment: tukey method for comparing a family of 13 estimates test(pairs(micsal.emm, by = "begin_date_year"), by = NULL, adjust = "mvt") contrast begin_date_year estimate SE df t.ratio p.value
age_group0 - age_group1 1992 -64.28 3.25 9471 -19.805 <.0001
age_group0 - age_group2 1992 -131.51 3.15 9471 -41.717 <.0001
age_group0 - age_group3 1992 -187.45 3.13 9471 -59.826 <.0001
age_group0 - age_group4 1992 -231.66 3.16 9471 -73.419 <.0001
age_group0 - age_group5 1992 -268.75 3.19 9471 -84.193 <.0001
age_group0 - age_group6 1992 -301.49 3.27 9471 -92.326 <.0001
age_group0 - age_group7 1992 -334.87 3.38 9471 -99.041 <.0001
age_group0 - age_group8 1992 -364.05 3.57 9471 -102.017 <.0001
age_group0 - age_group9 1992 -390.52 3.78 9471 -103.374 <.0001
age_group0 - age_group10 1992 -405.81 4.21 9471 -96.316 <.0001
age_group0 - age_group11 1992 -420.63 5.15 9471 -81.730 <.0001
age_group0 - age_group12 1992 -427.70 6.38 9471 -67.003 <.0001
age_group1 - age_group2 1992 -67.23 1.66 9471 -40.457 <.0001
age_group1 - age_group3 1992 -123.18 1.62 9471 -76.172 <.0001
age_group1 - age_group4 1992 -167.39 1.65 9471 -101.198 <.0001
age_group1 - age_group5 1992 -204.47 1.72 9471 -118.868 <.0001
age_group1 - age_group6 1992 -237.21 1.85 9471 -128.376 <.0001
age_group1 - age_group7 1992 -270.60 2.04 9471 -132.463 <.0001
age_group1 - age_group8 1992 -299.77 2.34 9471 -128.046 <.0001
age_group1 - age_group9 1992 -326.24 2.65 9471 -122.952 <.0001
age_group1 - age_group10 1992 -341.54 3.24 9471 -105.344 <.0001
age_group1 - age_group11 1992 -356.36 4.39 9471 -81.267 <.0001
age_group1 - age_group12 1992 -363.42 5.79 9471 -62.806 <.0001
age_group2 - age_group3 1992 -55.94 1.39 9471 -40.211 <.0001
age_group2 - age_group4 1992 -100.15 1.43 9471 -69.934 <.0001
age_group2 - age_group5 1992 -137.24 1.51 9471 -91.095 <.0001
age_group2 - age_group6 1992 -169.98 1.65 9471 -103.113 <.0001
age_group2 - age_group7 1992 -203.36 1.86 9471 -109.122 <.0001
age_group2 - age_group8 1992 -232.54 2.19 9471 -106.339 <.0001
age_group2 - age_group9 1992 -259.01 2.52 9471 -102.793 <.0001
age_group2 - age_group10 1992 -274.30 3.13 9471 -87.553 <.0001
age_group2 - age_group11 1992 -289.12 4.30 9471 -67.168 <.0001
age_group2 - age_group12 1992 -296.19 5.73 9471 -51.734 <.0001
age_group3 - age_group4 1992 -44.21 1.37 9471 -32.200 <.0001
age_group3 - age_group5 1992 -81.29 1.45 9471 -56.092 <.0001
age_group3 - age_group6 1992 -114.03 1.59 9471 -71.510 <.0001
age_group3 - age_group7 1992 -147.42 1.82 9471 -81.191 <.0001
age_group3 - age_group8 1992 -176.59 2.15 9471 -82.299 <.0001
age_group3 - age_group9 1992 -203.06 2.49 9471 -81.709 <.0001
age_group3 - age_group10 1992 -218.36 3.11 9471 -70.324 <.0001
age_group3 - age_group11 1992 -233.18 4.28 9471 -54.432 <.0001
age_group3 - age_group12 1992 -240.24 5.71 9471 -42.079 <.0001
age_group4 - age_group5 1992 -37.08 1.49 9471 -24.952 <.0001
age_group4 - age_group6 1992 -69.82 1.63 9471 -42.902 <.0001
age_group4 - age_group7 1992 -103.21 1.84 9471 -55.963 <.0001
age_group4 - age_group8 1992 -132.38 2.17 9471 -61.011 <.0001
age_group4 - age_group9 1992 -158.85 2.51 9471 -63.371 <.0001
age_group4 - age_group10 1992 -174.15 3.12 9471 -55.776 <.0001
age_group4 - age_group11 1992 -188.97 4.30 9471 -43.988 <.0001
age_group4 - age_group12 1992 -196.03 5.72 9471 -34.281 <.0001
age_group5 - age_group6 1992 -32.74 1.69 9471 -19.370 <.0001
age_group5 - age_group7 1992 -66.13 1.90 9471 -34.813 <.0001
age_group5 - age_group8 1992 -95.30 2.22 9471 -42.998 <.0001
age_group5 - age_group9 1992 -121.77 2.55 9471 -47.794 <.0001
age_group5 - age_group10 1992 -137.07 3.16 9471 -43.438 <.0001
age_group5 - age_group11 1992 -151.89 4.32 9471 -35.161 <.0001
age_group5 - age_group12 1992 -158.95 5.74 9471 -27.710 <.0001
age_group6 - age_group7 1992 -33.39 2.01 9471 -16.616 <.0001
age_group6 - age_group8 1992 -62.56 2.31 9471 -27.076 <.0001
age_group6 - age_group9 1992 -89.03 2.63 9471 -33.838 <.0001
age_group6 - age_group10 1992 -104.33 3.22 9471 -32.369 <.0001
age_group6 - age_group11 1992 -119.15 4.37 9471 -27.272 <.0001
age_group6 - age_group12 1992 -126.21 5.77 9471 -21.861 <.0001
age_group7 - age_group8 1992 -29.17 2.47 9471 -11.826 <.0001
age_group7 - age_group9 1992 -55.64 2.77 9471 -20.088 <.0001
age_group7 - age_group10 1992 -70.94 3.34 9471 -21.256 <.0001
age_group7 - age_group11 1992 -85.76 4.45 9471 -19.256 <.0001
age_group7 - age_group12 1992 -92.82 5.84 9471 -15.901 <.0001
age_group8 - age_group9 1992 -26.47 3.00 9471 -8.837 <.0001
age_group8 - age_group10 1992 -41.77 3.53 9471 -11.841 <.0001
age_group8 - age_group11 1992 -56.59 4.60 9471 -12.310 <.0001
age_group8 - age_group12 1992 -63.65 5.95 9471 -10.701 <.0001
age_group9 - age_group10 1992 -15.30 3.74 9471 -4.085 0.0026
age_group9 - age_group11 1992 -30.12 4.77 9471 -6.319 <.0001
age_group9 - age_group12 1992 -37.18 6.08 9471 -6.115 <.0001
age_group10 - age_group11 1992 -14.82 5.12 9471 -2.896 0.1315
age_group10 - age_group12 1992 -21.88 6.36 9471 -3.442 0.0260
age_group11 - age_group12 1992 -7.06 7.01 9471 -1.007 0.9981
P value adjustment: mvt method for 78 tests #export tables
# #interpret(eta_squared(micsal.lm), rules = "cohen1992") %>%
# write.csv(file = "Outputs/Tables/micsal_eta_squared.csv")
#slope contrasts
# Obtain slope estimates
micsal.slopes <- emtrends(micsal.lm, ~age_group, var = "begin_date_year")
# Compare estimated slopes to 0
micsal.slope.contrasts <- test(micsal.slopes) %>%
mutate(Species = "Largemouth Bass") %>%
rename(Age = age_group)
micsal.slope.contrasts %>%
write.csv(file = "Outputs/Tables/micsal_emmeans.csv")Plot raw data
(micsal.length.year.plot <- ggplot(data = micsal %>%
mutate(Age = as.factor(age_group)),
aes(x = begin_date_year, y = length_mean_mm,
color = Age, fill = Age, group = Age))+
#geom_point()+
geom_smooth(method = "lm")+
stat_poly_eq(use_label("eq"))+
scale_color_viridis_d()+
scale_fill_viridis_d()+
labs(y = "Length (mm)", x = "Year")+
theme_bw()
)`geom_smooth()` using formula = 'y ~ x'
Marginal Effects
result <- predict_response(micsal.lm, c("begin_date_year", "age_group"))
# plot(result)
# test_predictions(result) %>%
# write.csv(file = "Outputs/Tables/micsal_pairwise_length_time_slopes.csv", row.names = F)
(micsal.marginal.plot <- ggpredict(micsal.lm, terms = c("begin_date_year", "age_group")) %>%
plot()+
#stat_poly_eq(use_label("eq"))+
stat_regline_equation()+
stat_poly_line()+
# annotate(geom = "text", label = "y = 77 + 0.07x", x = 2000, y = 224)+
# annotate(geom = "text", label = "y = 21 + 0.093x", x = 2000, y = 212)+
# annotate(geom = "text", label = "y = -64 + 0.13x", x = 2000, y = 199)+
# annotate(geom = "text", label = "y = 230 - 0.028x", x = 2000, y = 182)+
# annotate(geom = "text", label = "y = 610 - 0.23x", x = 2000, y = 160)+
# annotate(geom = "text", label = "y = 920 - 0.39x", x = 2000, y = 137)+
# annotate(geom = "text", label = "y = 1200 - 0.55x", x = 2000, y = 110)+
# annotate(geom = "text", label = "y = 1700 - 0.83x", x = 2000, y = 80)+
scale_color_viridis_d()+
scale_fill_viridis_d()+
labs(title = NULL,
y = "Mean Length-at-Age (mm)",
x = "Year")+
guides(fill = guide_legend(title = "Age", reverse = T), color = guide_legend(title = "Age", reverse = T))+
theme_bw()
)Scale for colour is already present.
Adding another scale for colour, which will replace the existing scale.ggsave(filename = "~/GitHub/EAGER_growth/Outputs/Figures/Linear Models/micsal_marginal_effects_plot.tiff",
micsal.marginal.plot,
dpi = 300, width = 200, height = 150, units = "mm")
Northern Pike
Model
#filter data for only Black Crappie, Pomooxis nigromaculatus
esoluc <- all.grow.merge %>% filter(species == "northern_pike") %>%
filter(!age_group %in% c(11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22), #match age groups in BRTs
!is.na(age_group), !is.na(begin_date_year), !is.na(length_mean_mm),
!is.na(log_max_depth), !is.na(logarea))
#linear model
esoluc.lm <- lm(length_mean_mm ~ begin_date_year*age_group + log_max_depth + logarea + doy, data = esoluc)
summary(esoluc.lm)
Call:
lm(formula = length_mean_mm ~ begin_date_year * age_group + log_max_depth +
logarea + doy, data = esoluc)
Residuals:
Min 1Q Median 3Q Max
-489.85 -49.91 -4.95 44.36 624.55
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 3.374e+03 9.816e+02 3.437 0.000591 ***
begin_date_year -1.636e+00 4.960e-01 -3.297 0.000982 ***
age_group1 -8.287e+02 1.039e+03 -0.798 0.424954
age_group2 -1.659e+03 1.015e+03 -1.634 0.102255
age_group3 -2.223e+03 1.015e+03 -2.190 0.028542 *
age_group4 -1.366e+03 1.022e+03 -1.337 0.181332
age_group5 -4.319e+02 1.033e+03 -0.418 0.675799
age_group6 1.046e+03 1.051e+03 0.995 0.319531
age_group7 2.558e+03 1.095e+03 2.336 0.019539 *
age_group8 5.336e+03 1.183e+03 4.512 6.53e-06 ***
age_group9 4.732e+03 1.435e+03 3.296 0.000984 ***
age_group10 1.905e+03 1.737e+03 1.097 0.272846
log_max_depth 1.006e+01 1.391e+00 7.234 5.16e-13 ***
logarea 8.181e+00 6.768e-01 12.088 < 2e-16 ***
doy 2.626e-01 1.778e-02 14.766 < 2e-16 ***
begin_date_year:age_group1 4.803e-01 5.248e-01 0.915 0.360136
begin_date_year:age_group2 9.559e-01 5.130e-01 1.864 0.062431 .
begin_date_year:age_group3 1.277e+00 5.129e-01 2.490 0.012781 *
begin_date_year:age_group4 8.784e-01 5.163e-01 1.701 0.088902 .
begin_date_year:age_group5 4.365e-01 5.216e-01 0.837 0.402667
begin_date_year:age_group6 -2.789e-01 5.305e-01 -0.526 0.599121
begin_date_year:age_group7 -1.015e+00 5.525e-01 -1.837 0.066275 .
begin_date_year:age_group8 -2.378e+00 5.961e-01 -3.990 6.68e-05 ***
begin_date_year:age_group9 -2.037e+00 7.217e-01 -2.822 0.004783 **
begin_date_year:age_group10 -6.144e-01 8.713e-01 -0.705 0.480734
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 79.22 on 7096 degrees of freedom
(101 observations deleted due to missingness)
Multiple R-squared: 0.7401, Adjusted R-squared: 0.7392
F-statistic: 842 on 24 and 7096 DF, p-value: < 2.2e-16#calculate and interpret effect sizes
eta_squared(esoluc.lm) begin_date_year age_group log_max_depth logarea
0.003963165 0.712738629 0.003641321 0.004741566
doy begin_date_year:age_group
0.007271462 0.007746663 #interpret(eta_squared(esoluc.lm), rules = "cohen1992")
#calculate AIC score
AIC(esoluc.lm)[1] 82504.9#examine model fit
testDispersion(esoluc.lm)
DHARMa nonparametric dispersion test via sd of residuals fitted vs. simulated
data: simulationOutput
dispersion = 0.99729, p-value = 0.928
alternative hypothesis: two.sided
simulation.output <- simulateResiduals(fittedModel = esoluc.lm)
residuals(esoluc.lm) 1 2 3 4 5 6 7 8
2.659525e+01 5.619450e+01 -7.943255e+01 6.931526e+01 1.177336e+02 1.156359e+02 4.770489e+01 6.397266e+01
9 10 11 12 13 14 15 16
2.049132e+02 1.960052e+02 6.640842e+01 1.905955e+02 8.971069e+01 1.794074e+01 2.559577e+02 1.366532e+02
17 18 19 20 21 22 23 24
3.896028e+01 1.548345e+02 7.161573e-01 1.960778e+01 4.728282e+01 1.668983e+02 1.639843e+01 7.929778e+01
25 26 27 28 29 30 31 32
3.092325e+01 -4.115363e+00 3.682882e+01 4.431467e+01 1.747464e+01 -1.181367e-03 4.431467e+01 -6.655363e+00
33 34 35 36 37 38 39 40
-7.621181e+00 2.360453e+01 2.984382e+01 -9.831156e+01 4.613180e+01 2.687798e+00 1.977950e+01 -1.520674e+01
41 42 43 44 45 46 47 48
-5.449170e+01 -9.704156e+01 -6.019758e+01 -2.282674e+01 -8.497170e+01 -1.052826e+02 4.867180e+01 -6.719170e+01
49 50 51 52 53 54 55 56
-8.115258e+01 -9.133961e+01 9.693180e+01 -6.109561e-01 -1.154199e+01 3.532214e+01 -1.166074e-01 -3.648846e+01
57 58 59 60 61 62 63 64
-2.820418e+01 -3.731062e+00 -2.958460e+01 -4.622046e+01 9.786894e+01 2.165405e+00 1.346954e+01 4.011321e+01
65 66 67 68 69 70 71 72
1.723836e+02 -1.836681e-01 -1.460720e+01 -5.283307e+01 -8.206939e+01 -9.336274e+00 -3.772981e+01 -2.926688e+01
73 74 75 76 77 78 79 80
-3.818098e+00 -2.140060e+01 -2.814011e+01 -1.173970e+02 -1.162123e+02 -1.131243e+02 -8.339984e+01 1.764014e+02
81 82 83 84 85 86 87 88
1.391589e+02 2.112896e+02 1.230614e+02 2.358889e+01 1.509646e+02 5.575143e+01 -9.431113e+00 4.802732e+01
89 90 91 92 93 94 95 96
1.503296e+02 1.433814e+02 1.124889e+02 1.073855e+02 2.700171e+01 -1.457531e+02 2.869504e+01 4.429145e-01
97 98 99 100 101 102 103 104
4.621243e+01 -3.298110e+01 -5.825762e+01 4.554007e+01 -5.967049e+01 -7.930811e+01 -1.090941e+02 -1.822005e+02
105 106 107 108 109 110 111 112
3.968748e+01 -1.952346e+01 -1.343420e+01 -8.433730e+01 3.801050e+01 2.975879e+01 -7.290730e+01 3.991879e+01
113 114 115 116 117 118 119 120
9.762693e+01 5.897806e+01 -7.628445e+01 -1.724420e+01 -2.845730e+01 -1.130984e+02 4.245879e+01 2.379795e+01
121 122 123 124 125 126 127 128
1.136061e+02 -1.208388e+02 -2.986108e+01 4.756610e+01 -4.461081e+00 7.440650e+01 -1.684562e+01 8.281378e+01
129 130 131 132 133 134 135 136
7.186351e+01 5.747577e+01 3.104158e+01 -7.064371e+01 2.548126e+02 4.704757e+01 7.092916e+01 1.442323e+02
137 138 139 140 141 142 143 144
-5.800016e+00 9.706558e+00 -5.324774e+01 -2.836286e+01 1.150525e+02 3.819226e+01 9.101714e+01 -3.480750e+01
145 146 147 148 149 150 151 152
4.606089e+01 1.641473e+02 7.197142e+00 1.345250e+01 3.756007e+01 4.745457e+01 9.823344e+01 7.728172e+01
153 154 155 156 157 158 159 160
2.351027e+02 -2.538436e+01 5.121233e+00 6.076844e+01 1.179217e+02 -4.146656e+01 6.839172e+01 6.386987e+01
161 162 163 164 165 166 167 168
9.395160e+00 7.371095e+01 8.898031e+01 6.926083e+01 1.570251e+02 -9.543134e+01 1.065724e+02 8.277395e+01
169 170 171 172 173 174 175 176
1.017293e+02 -2.432274e+01 -2.085630e+01 -5.196312e+01 7.848489e+01 -9.936591e+01 -1.056458e+01 -3.248304e+01
177 178 179 180 181 182 183 184
-7.425660e+01 -9.620346e+01 -1.006067e+02 8.501143e+01 -5.882458e+01 -8.628486e+01 -2.834458e+01 1.851353e+01
185 186 187 188 189 190 191 192
-1.014081e+02 3.466341e+01 -7.534221e+01 -9.054350e+01 -1.577647e+01 -5.201611e+01 4.216028e+01 6.080124e+01
193 194 195 196 197 198 199 200
-2.058373e+01 -2.608259e+01 5.202569e+01 2.020205e+02 -3.791480e+01 -5.762587e+00 -3.602764e+01 1.152051e+01
201 202 203 204 205 206 207 208
8.320133e+01 2.788755e+02 1.395339e+02 8.548903e+01 1.436361e+02 4.430741e+01 7.696047e+01 -5.911146e+01
209 210 211 212 213 214 215 216
-1.852366e+02 1.052674e+02 -9.328670e-01 2.961413e+01 8.351228e+01 -1.151566e+02 3.122516e+01 -7.398276e+01
217 218 219 220 221 222 223 224
-3.929706e+01 -6.896101e+01 2.310492e+01 -7.063018e+01 5.084179e+01 4.468818e+01 6.358090e+01 -1.094066e+01
225 226 227 228 229 230 231 232
-1.000721e+01 6.751762e+01 3.961803e+01 1.518679e+02 3.607792e+01 6.401792e+01 4.478955e+01 8.673104e+00
233 234 235 236 237 238 239 240
5.092953e+01 -3.055619e+01 3.984729e+01 1.086373e+02 8.224769e+01 3.125393e+01 1.006105e+02 4.154469e+01
241 242 243 244 245 246 247 248
2.925492e+02 1.298567e+02 1.147602e+02 7.716769e+01 2.158815e+02 2.637947e+02 1.706292e+02 1.006105e+02
249 250 251 252 253 254 255 256
1.059425e+02 2.168047e+02 4.120776e+01 9.279343e+01 5.585318e+01 2.073170e+02 5.385293e+01 1.618578e+02
257 258 259 260 261 262 263 264
7.247343e+01 1.627448e+02 6.279776e+01 7.399743e+01 5.521818e+01 7.015700e+01 1.192080e+02 4.385902e+01
265 266 267 268 269 270 271 272
-4.812463e+01 -2.126302e+01 5.863974e+01 1.277216e+01 4.106101e+00 -1.558402e+01 9.684935e+01 -1.845095e+02
273 274 275 276 277 278 279 280
1.149437e+01 1.011713e+01 -4.000451e+00 1.054435e+02 -4.608048e+01 1.086709e+00 -2.533326e+01 1.152264e+02
281 282 283 284 285 286 287 288
-1.088416e+02 -1.021832e+02 9.316945e-01 -1.036094e+02 -2.246066e+02 -1.319642e+02 5.249419e+01 8.448909e+01
289 290 291 292 293 294 295 296
-1.470520e+02 -7.011327e+01 -2.581364e+01 -1.141174e+01 -1.177482e+02 -7.498895e+01 -9.992463e+01 -1.100077e+02
297 298 299 300 301 302 303 304
-1.334678e+02 -1.608539e+02 -1.745069e+02 -9.170339e+01 5.953589e+01 3.908583e+01 1.012117e+02 2.571404e+01
305 306 307 308 309 310 311 312
3.041558e+01 -2.477047e+01 1.137730e+01 -7.586185e+01 -1.163302e+02 -6.884893e+01 -4.715865e+01 -3.729830e+01
313 314 315 316 317 318 319 320
-4.396076e+01 4.340615e+01 -6.382719e+01 2.308615e+01 -3.039465e+01 -3.772100e+01 8.238077e+01 6.070394e+01
321 322 323 324 325 326 327 328
1.931759e+01 5.080818e+01 -4.280984e+00 9.181295e+01 -3.596193e+01 1.471910e+02 2.428566e+02 -5.986565e+01
329 330 331 332 333 334 335 336
1.893451e+02 3.916772e+01 -6.801087e+01 8.571449e+01 4.075966e+01 5.594637e+01 3.661604e+01 4.819361e+01
337 338 339 340 341 342 343 344
9.230915e+01 4.646149e+01 6.093132e+01 9.675829e+00 -1.016960e+02 8.749727e+01 1.150804e+02 -5.908597e+01
345 346 347 348 349 350 351 352
2.408648e+01 2.727403e+01 -6.524133e+01 -5.590240e+01 -6.393523e+00 -4.329709e+01 7.821800e+00 1.490522e+02
353 354 355 356 357 358 359 360
-7.500942e+01 1.020589e+02 7.658824e+00 -3.182942e+01 -2.282442e+01 -6.448669e-01 1.257656e+02 5.072224e+01
361 362 363 364 365 366 367 368
1.435656e+01 4.485133e+00 -6.077916e+01 -3.990568e+01 3.213585e+01 -3.819337e+01 2.373921e+01 -3.055553e+01
369 370 371 372 373 374 375 376
-3.099995e+01 5.896512e+00 8.818519e+01 7.118960e+01 1.599959e+00 -4.441746e+01 5.335518e+01 -1.028700e+02
377 378 379 380 381 382 383 384
-3.201595e+01 -2.787040e+01 -7.713375e+00 -3.552746e+01 -2.611053e+01 -5.206996e+01 -7.544995e+01 2.112919e+01
385 386 387 388 389 390 391 392
6.024947e+01 -2.150835e+02 -3.115574e+00 1.125723e+02 8.096303e+01 2.150947e+01 -4.843490e+00 1.234810e+02
393 394 395 396 397 398 399 400
-2.770349e+01 1.332695e+02 -4.542901e+01 9.701292e+01 9.715363e+01 6.987340e+01 1.502760e+02 9.150959e+01
401 402 403 404 405 406 407 408
1.227530e+01 2.011067e+02 5.987260e+01 6.102959e+01 3.958030e+01 9.950673e+01 -8.126224e+00 1.646483e+02
409 410 411 412 413 414 415 416
1.056869e+01 -1.841531e+01 -2.188931e+01 -4.451059e+01 4.397813e+01 7.133234e+00 -3.159531e+01 -4.403303e+01
417 418 419 420 421 422 423 424
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425 426 427 428 429 430 431 432
1.681285e+01 -1.161594e+02 1.850300e+02 8.136128e+01 -6.040944e+01 -4.726249e+01 4.420290e+01 5.105191e+01
433 434 435 436 437 438 439 440
8.804799e+01 1.712742e+01 -5.261329e+00 1.402209e+02 2.814097e+01 -1.544423e+02 2.091643e+02 -3.284696e+01
441 442 443 444 445 446 447 448
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449 450 451 452 453 454 455 456
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457 458 459 460 461 462 463 464
1.149244e+02 8.917768e+00 7.149943e+01 -3.363445e+00 1.553579e+00 -7.210503e+00 -7.526358e+01 2.979866e+01
465 466 467 468 469 470 471 472
8.971766e+01 6.685766e+01 7.873888e+01 3.113007e+02 -1.361964e+02 -1.196830e+02 1.090003e+01 -5.783725e+01
473 474 475 476 477 478 479 480
-6.874816e+01 3.256107e+01 7.832149e+01 3.865031e+01 1.067513e+02 1.663124e+01 6.685107e+01 -1.989184e+01
481 482 483 484 485 486 487 488
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489 490 491 492 493 494 495 496
-4.263615e+00 6.978509e+00 -4.617361e+01 9.334764e+01 7.801348e+01 8.825851e+01 -3.619236e+01 8.563778e+01
497 498 499 500 501 502 503 504
1.262515e+02 1.937090e+02 4.361933e+01 -1.006587e+02 9.356490e+01 -1.438387e+02 -9.303875e+01 4.977673e+01
505 506 507 508 509 510 511 512
1.134762e+02 -5.369258e+01 5.853671e+01 6.179079e+01 6.876356e+01 1.047948e+02 -9.158590e+01 -9.667651e+01
513 514 515 516 517 518 519 520
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521 522 523 524 525 526 527 528
8.922255e+01 -3.311024e+01 6.942974e+01 6.066566e+01 7.135259e+01 9.319230e+01 7.323974e+01 -9.184340e+00
529 530 531 532 533 534 535 536
5.103259e+01 1.120682e+02 2.963316e+01 7.831974e+01 1.101957e+02 -1.043883e+02 2.723187e+01 -4.115276e+01
537 538 539 540 541 542 543 544
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545 546 547 548 549 550 551 552
1.471142e+02 2.532120e+02 2.686130e+01 -2.377648e+01 -4.089471e+01 -5.522157e+01 -4.743286e+01 -6.020266e+01
553 554 555 556 557 558 559 560
1.500293e+02 1.737050e+01 1.065867e+02 -7.854174e+01 -1.222237e+02 -8.693662e+01 -8.920366e+01 -2.266174e+01
561 562 563 564 565 566 567 568
-2.803282e+01 -7.096786e+01 -4.469330e+01 -8.430881e+01 6.679515e+01 9.220513e+00 -8.366786e+01 -7.855997e+01
569 570 571 572 573 574 575 576
-1.317221e+02 -1.516448e+02 1.231942e+02 1.426847e+02 -8.601063e+01 -1.046288e+02 1.790742e+02 8.592257e+01
577 578 579 580 581 582 583 584
1.131242e+02 2.257338e+02 -5.120818e+01 -2.225894e+01 -2.146794e+01 -1.806369e+02 3.401819e+00 -2.162505e+00
585 586 587 588 589 590 591 592
-1.772443e+01 -1.899323e+01 -1.040179e+02 5.134376e+01 4.611618e+01 1.440696e+02 3.451615e+01 1.676200e+02
593 594 595 596 597 598 599 600
7.970709e+01 7.151618e+01 1.040193e+02 4.298281e+01 -2.855900e+01 5.337576e+01 3.837471e+00 7.584117e+01
601 602 603 604 605 606 607 608
9.708036e+01 1.160118e+02 5.731001e+00 4.033709e+01 1.087695e+02 1.660588e+02 2.816615e+01 -6.369994e+00
609 610 611 612 613 614 615 616
-6.517118e+01 -4.479879e+01 -4.739118e+01 -7.882432e+01 -6.062626e+01 7.906215e+00 -3.215118e+01 1.801382e+01
617 618 619 620 621 622 623 624
8.410621e+01 3.311102e+01 7.886649e+01 4.808751e+00 4.303329e+01 5.252686e+01 1.427301e+02 -6.813082e+01
625 626 627 628 629 630 631 632
-1.034792e+02 -1.106195e+02 -1.383181e+02 -7.278672e+01 7.731929e+01 -4.985303e+01 -3.351860e+01 -1.446106e+02
633 634 635 636 637 638 639 640
-1.695208e+02 -1.196528e+02 -4.503259e+01 -1.332512e+02 -5.530082e+00 -2.278654e+02 1.143310e+02 4.228526e+01
641 642 643 644 645 646 647 648
1.425124e+02 -5.146886e+00 -2.192988e+01 -4.149881e+00 1.269350e+00 4.824935e+01 5.958372e+01 5.053674e+01
649 650 651 652 653 654 655 656
2.122112e+01 1.874032e+02 1.385034e+02 4.900414e+01 3.020137e+00 -1.968149e+01 8.418560e+01 1.033206e+02
657 658 659 660 661 662 663 664
1.249198e+02 -6.557172e+01 5.451802e-01 -6.808101e+01 5.380828e+01 -6.718815e+01 -4.581010e+00 -1.028661e+02
665 666 667 668 669 670 671 672
-7.074820e+00 -7.212744e+01 5.373839e+01 4.862819e+01 4.017677e+01 3.662266e+01 9.519024e+01 9.650310e+01
673 674 675 676 677 678 679 680
-6.407732e+01 -1.438551e+02 1.166747e+02 -2.142804e+01 -8.315275e+01 -1.188990e+02 -8.315186e+01 -4.179086e+01
681 682 683 684 685 686 687 688
-2.541045e+01 4.242996e+01 1.432698e+01 1.445290e+01 1.051498e+02 1.128454e+02 -3.079588e+01 -3.773136e+01
689 690 691 692 693 694 695 696
-6.646564e+00 -1.608576e+01 1.309791e+01 -1.977451e+01 -2.227165e+01 -1.165823e+02 -2.213930e+01 -7.775077e+01
697 698 699 700 701 702 703 704
-5.887204e+01 -1.047197e+02 -7.077654e+01 4.693425e+01 6.049914e+01 3.793863e+01 4.782572e+01 3.478742e+01
705 706 707 708 709 710 711 712
2.441090e+01 4.410988e+01 -5.243716e+01 -8.105495e+01 2.519899e+01 5.374105e+00 -2.725748e+01 5.813263e+01
713 714 715 716 717 718 719 720
5.673995e+01 1.355445e+01 -5.474153e+01 -6.294502e+01 -3.255968e+01 5.028360e-01 2.077962e+01 3.475748e+01
721 722 723 724 725 726 727 728
2.600174e+01 1.053896e+02 1.102056e+02 7.081253e+01 5.398014e+01 4.425154e+01 1.748111e+01 1.198300e+01
729 730 731 732 733 734 735 736
-6.122807e+01 -1.209392e+02 -3.140091e+01 -5.497276e+01 7.308011e+01 6.529808e+01 2.500463e+02 6.037421e+01
737 738 739 740 741 742 743 744
3.963548e+01 9.194688e+01 8.892872e+00 7.602297e+01 7.373783e-01 -3.157584e+01 -5.119954e+01 2.959791e+01
745 746 747 748 749 750 751 752
-1.878938e+01 -1.723897e+01 3.760871e+00 3.431364e+01 -4.100361e+01 -5.487014e+00 -2.411295e+00 -2.002220e+01
753 754 755 756 757 758 759 760
2.382353e+01 2.327254e+01 2.150210e+00 -1.108688e+02 1.038216e+02 9.764385e+00 -3.823196e+01 -2.363731e+01
761 762 763 764 765 766 767 768
-5.523014e+00 2.260199e+01 -7.690865e+00 1.111401e+02 1.144271e+01 -2.138782e+01 1.185238e+02 4.280349e+01
769 770 771 772 773 774 775 776
1.174851e+02 1.319449e+02 8.740961e+01 2.028926e+02 3.470376e+01 -3.765136e-01 2.265842e+01 1.242396e+02
777 778 779 780 781 782 783 784
1.659420e+02 1.877584e+02 6.590489e+01 3.914961e+01 -7.466426e+01 8.085835e+01 -6.036729e+00 2.025903e+02
785 786 787 788 789 790 791 792
6.615957e+01 8.516037e+01 1.539357e+02 -2.074165e+01 -4.391979e+01 7.100994e+01 -1.048085e+02 1.425349e+01
793 794 795 796 797 798 799 800
9.145853e+01 2.565363e+01 3.348566e+01 2.332080e+01 6.561234e+01 9.941802e+01 8.143705e+01 3.233158e+01
801 802 803 804 805 806 807 808
-5.958742e+01 1.348437e+02 -5.126123e+01 -2.526504e+01 -5.084379e+01 -1.397234e+02 -1.256504e+01 -2.101277e+01
809 810 811 812 813 814 815 816
1.269562e+02 5.392294e+01 1.475087e+02 1.394070e+02 2.542537e+02 -6.815111e+01 -2.819940e+01 -3.947898e+01
817 818 819 820 821 822 823 824
-2.401283e+01 -4.723047e+01 1.437852e+01 2.569215e+02 -2.407825e+01 -6.335409e+01 -3.295795e+01 -3.949582e+01
825 826 827 828 829 830 831 832
-2.743568e+01 1.166312e+02 2.616549e+01 1.144067e+02 2.331239e+00 7.538822e+00 1.958774e+02 6.354993e+01
833 834 835 836 837 838 839 840
5.397791e+01 1.261882e+01 1.018965e+02 2.644574e+02 -1.952863e+00 6.040305e+01 6.854998e+01 -2.535445e+01
841 842 843 844 845 846 847 848
-1.973286e+01 2.103305e+01 -5.110021e+00 6.332205e+01 5.203151e+01 1.067265e+02 1.264067e+02 1.402567e+01
849 850 851 852 853 854 855 856
-1.282414e+02 2.496160e+02 1.490624e+02 1.069952e+02 -1.261540e+01 1.298303e+02 1.158330e+02 2.210646e+02
857 858 859 860 861 862 863 864
7.903032e+01 1.132930e+02 7.647984e+01 9.123551e+01 1.017907e+01 1.266777e+01 -1.542462e+02 5.253126e+01
865 866 867 868 869 870 871 872
2.055355e+02 8.541026e+01 1.271907e+01 8.100776e-01 -8.199893e+01 1.720376e+01 1.031498e+02 6.126351e+01
873 874 875 876 877 878 879 880
8.256907e+01 7.447008e+01 -8.215890e+01 -3.627893e+01 -9.466236e+00 -5.967049e+01 -5.825762e+01 4.554007e+01
881 882 883 884 885 886 887 888
-5.587627e+01 -3.002733e+01 -3.174179e+01 -1.798018e+01 -3.258294e+00 4.285872e+01 -1.626038e+01 -1.275354e+01
889 890 891 892 893 894 895 896
-4.721001e+00 9.144891e+00 1.139872e+02 -9.108100e+01 1.524891e+00 -9.877582e+01 -1.651340e+02 -2.009109e+02
897 898 899 900 901 902 903 904
-4.621379e+01 -6.512082e+01 -6.887842e+01 -7.868673e+01 -9.443729e+01 -3.835089e+01 6.752552e+00 -4.988082e+01
905 906 907 908 909 910 911 912
-3.204842e+01 -7.676248e+01 -1.016340e+02 -2.059909e+02 -6.251049e+00 -8.984816e+00 -1.291572e+02 -1.333141e+02
913 914 915 916 917 918 919 920
-5.559783e+01 -5.982033e+00 -1.184034e+02 -2.736099e+01 -5.360703e+01 -3.195424e+01 -2.084964e+01 -3.992596e+01
921 922 923 924 925 926 927 928
2.740487e+02 2.710047e+02 5.365480e+01 1.028226e+02 2.501512e+02 -3.736606e+01 -4.901619e+01 -7.176977e+01
929 930 931 932 933 934 935 936
-4.402916e+01 -3.193480e+01 4.018559e+01 5.204913e+01 7.903307e+00 -1.967508e+00 -3.029764e+01 2.374423e+01
937 938 939 940 941 942 943 944
-2.994411e+00 3.807913e+01 3.697916e+01 6.474913e+01 -4.229577e+01 -6.593686e+01 -6.927039e+01 -1.115493e+02
945 946 947 948 949 950 951 952
-1.185011e+02 -7.318212e+01 4.374159e+01 -8.909625e+01 -1.036645e+02 -1.088486e+02 -1.081496e+02 -9.831617e+01
953 954 955 956 957 958 959 960
-1.317263e+02 -2.226445e+02 -9.074015e+01 -8.922170e+01 -1.040276e+02 -1.227691e+02 -9.712424e+01 1.896751e+01
961 962 963 964 965 966 967 968
-9.184942e+01 -1.229276e+02 -1.271502e+02 -8.747175e+01 -1.601248e+02 -1.159912e+02 -9.906025e+01 -6.741435e+01
969 970 971 972 973 974 975 976
-1.490844e+02 -9.932051e+01 1.637759e+01 7.278451e+01 9.779111e+01 6.643280e+01 4.556030e+01 5.969428e+01
977 978 979 980 981 982 983 984
-2.476180e+01 8.574712e+01 3.005770e+01 -1.178587e+01 7.892625e+01 1.736803e+01 -7.036608e+01 1.017248e+01
985 986 987 988 989 990 991 992
7.578803e+01 2.076549e+01 -2.210608e+01 3.362958e+01 7.716032e+00 2.203549e+01 -6.782608e+01 -2.445122e+02
993 994 995 996 997 998 999 1000
-8.399709e+01 -4.376110e+01 -2.013670e+02 -7.566329e+01 -1.199657e+02 1.390746e-01 -4.838757e+01 -8.821110e+01
[ reached 'max' / getOption("max.print") -- omitted 6121 entries ]residuals(esoluc.lm, quantileFunction = qnorm) 1 2 3 4 5 6 7 8
2.659525e+01 5.619450e+01 -7.943255e+01 6.931526e+01 1.177336e+02 1.156359e+02 4.770489e+01 6.397266e+01
9 10 11 12 13 14 15 16
2.049132e+02 1.960052e+02 6.640842e+01 1.905955e+02 8.971069e+01 1.794074e+01 2.559577e+02 1.366532e+02
17 18 19 20 21 22 23 24
3.896028e+01 1.548345e+02 7.161573e-01 1.960778e+01 4.728282e+01 1.668983e+02 1.639843e+01 7.929778e+01
25 26 27 28 29 30 31 32
3.092325e+01 -4.115363e+00 3.682882e+01 4.431467e+01 1.747464e+01 -1.181367e-03 4.431467e+01 -6.655363e+00
33 34 35 36 37 38 39 40
-7.621181e+00 2.360453e+01 2.984382e+01 -9.831156e+01 4.613180e+01 2.687798e+00 1.977950e+01 -1.520674e+01
41 42 43 44 45 46 47 48
-5.449170e+01 -9.704156e+01 -6.019758e+01 -2.282674e+01 -8.497170e+01 -1.052826e+02 4.867180e+01 -6.719170e+01
49 50 51 52 53 54 55 56
-8.115258e+01 -9.133961e+01 9.693180e+01 -6.109561e-01 -1.154199e+01 3.532214e+01 -1.166074e-01 -3.648846e+01
57 58 59 60 61 62 63 64
-2.820418e+01 -3.731062e+00 -2.958460e+01 -4.622046e+01 9.786894e+01 2.165405e+00 1.346954e+01 4.011321e+01
65 66 67 68 69 70 71 72
1.723836e+02 -1.836681e-01 -1.460720e+01 -5.283307e+01 -8.206939e+01 -9.336274e+00 -3.772981e+01 -2.926688e+01
73 74 75 76 77 78 79 80
-3.818098e+00 -2.140060e+01 -2.814011e+01 -1.173970e+02 -1.162123e+02 -1.131243e+02 -8.339984e+01 1.764014e+02
81 82 83 84 85 86 87 88
1.391589e+02 2.112896e+02 1.230614e+02 2.358889e+01 1.509646e+02 5.575143e+01 -9.431113e+00 4.802732e+01
89 90 91 92 93 94 95 96
1.503296e+02 1.433814e+02 1.124889e+02 1.073855e+02 2.700171e+01 -1.457531e+02 2.869504e+01 4.429145e-01
97 98 99 100 101 102 103 104
4.621243e+01 -3.298110e+01 -5.825762e+01 4.554007e+01 -5.967049e+01 -7.930811e+01 -1.090941e+02 -1.822005e+02
105 106 107 108 109 110 111 112
3.968748e+01 -1.952346e+01 -1.343420e+01 -8.433730e+01 3.801050e+01 2.975879e+01 -7.290730e+01 3.991879e+01
113 114 115 116 117 118 119 120
9.762693e+01 5.897806e+01 -7.628445e+01 -1.724420e+01 -2.845730e+01 -1.130984e+02 4.245879e+01 2.379795e+01
121 122 123 124 125 126 127 128
1.136061e+02 -1.208388e+02 -2.986108e+01 4.756610e+01 -4.461081e+00 7.440650e+01 -1.684562e+01 8.281378e+01
129 130 131 132 133 134 135 136
7.186351e+01 5.747577e+01 3.104158e+01 -7.064371e+01 2.548126e+02 4.704757e+01 7.092916e+01 1.442323e+02
137 138 139 140 141 142 143 144
-5.800016e+00 9.706558e+00 -5.324774e+01 -2.836286e+01 1.150525e+02 3.819226e+01 9.101714e+01 -3.480750e+01
145 146 147 148 149 150 151 152
4.606089e+01 1.641473e+02 7.197142e+00 1.345250e+01 3.756007e+01 4.745457e+01 9.823344e+01 7.728172e+01
153 154 155 156 157 158 159 160
2.351027e+02 -2.538436e+01 5.121233e+00 6.076844e+01 1.179217e+02 -4.146656e+01 6.839172e+01 6.386987e+01
161 162 163 164 165 166 167 168
9.395160e+00 7.371095e+01 8.898031e+01 6.926083e+01 1.570251e+02 -9.543134e+01 1.065724e+02 8.277395e+01
169 170 171 172 173 174 175 176
1.017293e+02 -2.432274e+01 -2.085630e+01 -5.196312e+01 7.848489e+01 -9.936591e+01 -1.056458e+01 -3.248304e+01
177 178 179 180 181 182 183 184
-7.425660e+01 -9.620346e+01 -1.006067e+02 8.501143e+01 -5.882458e+01 -8.628486e+01 -2.834458e+01 1.851353e+01
185 186 187 188 189 190 191 192
-1.014081e+02 3.466341e+01 -7.534221e+01 -9.054350e+01 -1.577647e+01 -5.201611e+01 4.216028e+01 6.080124e+01
193 194 195 196 197 198 199 200
-2.058373e+01 -2.608259e+01 5.202569e+01 2.020205e+02 -3.791480e+01 -5.762587e+00 -3.602764e+01 1.152051e+01
201 202 203 204 205 206 207 208
8.320133e+01 2.788755e+02 1.395339e+02 8.548903e+01 1.436361e+02 4.430741e+01 7.696047e+01 -5.911146e+01
209 210 211 212 213 214 215 216
-1.852366e+02 1.052674e+02 -9.328670e-01 2.961413e+01 8.351228e+01 -1.151566e+02 3.122516e+01 -7.398276e+01
217 218 219 220 221 222 223 224
-3.929706e+01 -6.896101e+01 2.310492e+01 -7.063018e+01 5.084179e+01 4.468818e+01 6.358090e+01 -1.094066e+01
225 226 227 228 229 230 231 232
-1.000721e+01 6.751762e+01 3.961803e+01 1.518679e+02 3.607792e+01 6.401792e+01 4.478955e+01 8.673104e+00
233 234 235 236 237 238 239 240
5.092953e+01 -3.055619e+01 3.984729e+01 1.086373e+02 8.224769e+01 3.125393e+01 1.006105e+02 4.154469e+01
241 242 243 244 245 246 247 248
2.925492e+02 1.298567e+02 1.147602e+02 7.716769e+01 2.158815e+02 2.637947e+02 1.706292e+02 1.006105e+02
249 250 251 252 253 254 255 256
1.059425e+02 2.168047e+02 4.120776e+01 9.279343e+01 5.585318e+01 2.073170e+02 5.385293e+01 1.618578e+02
257 258 259 260 261 262 263 264
7.247343e+01 1.627448e+02 6.279776e+01 7.399743e+01 5.521818e+01 7.015700e+01 1.192080e+02 4.385902e+01
265 266 267 268 269 270 271 272
-4.812463e+01 -2.126302e+01 5.863974e+01 1.277216e+01 4.106101e+00 -1.558402e+01 9.684935e+01 -1.845095e+02
273 274 275 276 277 278 279 280
1.149437e+01 1.011713e+01 -4.000451e+00 1.054435e+02 -4.608048e+01 1.086709e+00 -2.533326e+01 1.152264e+02
281 282 283 284 285 286 287 288
-1.088416e+02 -1.021832e+02 9.316945e-01 -1.036094e+02 -2.246066e+02 -1.319642e+02 5.249419e+01 8.448909e+01
289 290 291 292 293 294 295 296
-1.470520e+02 -7.011327e+01 -2.581364e+01 -1.141174e+01 -1.177482e+02 -7.498895e+01 -9.992463e+01 -1.100077e+02
297 298 299 300 301 302 303 304
-1.334678e+02 -1.608539e+02 -1.745069e+02 -9.170339e+01 5.953589e+01 3.908583e+01 1.012117e+02 2.571404e+01
305 306 307 308 309 310 311 312
3.041558e+01 -2.477047e+01 1.137730e+01 -7.586185e+01 -1.163302e+02 -6.884893e+01 -4.715865e+01 -3.729830e+01
313 314 315 316 317 318 319 320
-4.396076e+01 4.340615e+01 -6.382719e+01 2.308615e+01 -3.039465e+01 -3.772100e+01 8.238077e+01 6.070394e+01
321 322 323 324 325 326 327 328
1.931759e+01 5.080818e+01 -4.280984e+00 9.181295e+01 -3.596193e+01 1.471910e+02 2.428566e+02 -5.986565e+01
329 330 331 332 333 334 335 336
1.893451e+02 3.916772e+01 -6.801087e+01 8.571449e+01 4.075966e+01 5.594637e+01 3.661604e+01 4.819361e+01
337 338 339 340 341 342 343 344
9.230915e+01 4.646149e+01 6.093132e+01 9.675829e+00 -1.016960e+02 8.749727e+01 1.150804e+02 -5.908597e+01
345 346 347 348 349 350 351 352
2.408648e+01 2.727403e+01 -6.524133e+01 -5.590240e+01 -6.393523e+00 -4.329709e+01 7.821800e+00 1.490522e+02
353 354 355 356 357 358 359 360
-7.500942e+01 1.020589e+02 7.658824e+00 -3.182942e+01 -2.282442e+01 -6.448669e-01 1.257656e+02 5.072224e+01
361 362 363 364 365 366 367 368
1.435656e+01 4.485133e+00 -6.077916e+01 -3.990568e+01 3.213585e+01 -3.819337e+01 2.373921e+01 -3.055553e+01
369 370 371 372 373 374 375 376
-3.099995e+01 5.896512e+00 8.818519e+01 7.118960e+01 1.599959e+00 -4.441746e+01 5.335518e+01 -1.028700e+02
377 378 379 380 381 382 383 384
-3.201595e+01 -2.787040e+01 -7.713375e+00 -3.552746e+01 -2.611053e+01 -5.206996e+01 -7.544995e+01 2.112919e+01
385 386 387 388 389 390 391 392
6.024947e+01 -2.150835e+02 -3.115574e+00 1.125723e+02 8.096303e+01 2.150947e+01 -4.843490e+00 1.234810e+02
393 394 395 396 397 398 399 400
-2.770349e+01 1.332695e+02 -4.542901e+01 9.701292e+01 9.715363e+01 6.987340e+01 1.502760e+02 9.150959e+01
401 402 403 404 405 406 407 408
1.227530e+01 2.011067e+02 5.987260e+01 6.102959e+01 3.958030e+01 9.950673e+01 -8.126224e+00 1.646483e+02
409 410 411 412 413 414 415 416
1.056869e+01 -1.841531e+01 -2.188931e+01 -4.451059e+01 4.397813e+01 7.133234e+00 -3.159531e+01 -4.403303e+01
417 418 419 420 421 422 423 424
-5.149241e+01 1.397291e+01 -6.333687e+01 6.745823e+01 -3.882603e+01 -1.400146e+02 3.338130e+00 9.402723e+01
425 426 427 428 429 430 431 432
1.681285e+01 -1.161594e+02 1.850300e+02 8.136128e+01 -6.040944e+01 -4.726249e+01 4.420290e+01 5.105191e+01
433 434 435 436 437 438 439 440
8.804799e+01 1.712742e+01 -5.261329e+00 1.402209e+02 2.814097e+01 -1.544423e+02 2.091643e+02 -3.284696e+01
441 442 443 444 445 446 447 448
-1.920103e+00 -5.329808e+01 -5.863537e+01 -6.473559e+01 -1.045416e+02 -1.245885e+02 1.184341e+00 -2.662808e+01
449 450 451 452 453 454 455 456
-6.471323e+01 -6.623353e+01 -3.333091e+01 -1.659495e+02 -7.755566e+01 5.063379e+01 1.174379e+02 1.865054e+01
457 458 459 460 461 462 463 464
1.149244e+02 8.917768e+00 7.149943e+01 -3.363445e+00 1.553579e+00 -7.210503e+00 -7.526358e+01 2.979866e+01
465 466 467 468 469 470 471 472
8.971766e+01 6.685766e+01 7.873888e+01 3.113007e+02 -1.361964e+02 -1.196830e+02 1.090003e+01 -5.783725e+01
473 474 475 476 477 478 479 480
-6.874816e+01 3.256107e+01 7.832149e+01 3.865031e+01 1.067513e+02 1.663124e+01 6.685107e+01 -1.989184e+01
481 482 483 484 485 486 487 488
-3.246969e+01 1.880313e+02 1.203023e+02 -1.787042e+01 5.542107e+01 6.763675e+01 -1.142518e+01 8.643131e+01
489 490 491 492 493 494 495 496
-4.263615e+00 6.978509e+00 -4.617361e+01 9.334764e+01 7.801348e+01 8.825851e+01 -3.619236e+01 8.563778e+01
497 498 499 500 501 502 503 504
1.262515e+02 1.937090e+02 4.361933e+01 -1.006587e+02 9.356490e+01 -1.438387e+02 -9.303875e+01 4.977673e+01
505 506 507 508 509 510 511 512
1.134762e+02 -5.369258e+01 5.853671e+01 6.179079e+01 6.876356e+01 1.047948e+02 -9.158590e+01 -9.667651e+01
513 514 515 516 517 518 519 520
-5.628608e+01 -2.344096e+01 -4.803498e+00 8.821493e+01 -7.703038e+01 -5.317646e+01 -9.167107e+01 -4.908864e+01
521 522 523 524 525 526 527 528
8.922255e+01 -3.311024e+01 6.942974e+01 6.066566e+01 7.135259e+01 9.319230e+01 7.323974e+01 -9.184340e+00
529 530 531 532 533 534 535 536
5.103259e+01 1.120682e+02 2.963316e+01 7.831974e+01 1.101957e+02 -1.043883e+02 2.723187e+01 -4.115276e+01
537 538 539 540 541 542 543 544
-4.375152e+00 1.848485e+01 -2.591276e+01 -6.089015e+01 -1.424883e+02 1.386475e+02 4.598506e+01 1.957451e+02
545 546 547 548 549 550 551 552
1.471142e+02 2.532120e+02 2.686130e+01 -2.377648e+01 -4.089471e+01 -5.522157e+01 -4.743286e+01 -6.020266e+01
553 554 555 556 557 558 559 560
1.500293e+02 1.737050e+01 1.065867e+02 -7.854174e+01 -1.222237e+02 -8.693662e+01 -8.920366e+01 -2.266174e+01
561 562 563 564 565 566 567 568
-2.803282e+01 -7.096786e+01 -4.469330e+01 -8.430881e+01 6.679515e+01 9.220513e+00 -8.366786e+01 -7.855997e+01
569 570 571 572 573 574 575 576
-1.317221e+02 -1.516448e+02 1.231942e+02 1.426847e+02 -8.601063e+01 -1.046288e+02 1.790742e+02 8.592257e+01
577 578 579 580 581 582 583 584
1.131242e+02 2.257338e+02 -5.120818e+01 -2.225894e+01 -2.146794e+01 -1.806369e+02 3.401819e+00 -2.162505e+00
585 586 587 588 589 590 591 592
-1.772443e+01 -1.899323e+01 -1.040179e+02 5.134376e+01 4.611618e+01 1.440696e+02 3.451615e+01 1.676200e+02
593 594 595 596 597 598 599 600
7.970709e+01 7.151618e+01 1.040193e+02 4.298281e+01 -2.855900e+01 5.337576e+01 3.837471e+00 7.584117e+01
601 602 603 604 605 606 607 608
9.708036e+01 1.160118e+02 5.731001e+00 4.033709e+01 1.087695e+02 1.660588e+02 2.816615e+01 -6.369994e+00
609 610 611 612 613 614 615 616
-6.517118e+01 -4.479879e+01 -4.739118e+01 -7.882432e+01 -6.062626e+01 7.906215e+00 -3.215118e+01 1.801382e+01
617 618 619 620 621 622 623 624
8.410621e+01 3.311102e+01 7.886649e+01 4.808751e+00 4.303329e+01 5.252686e+01 1.427301e+02 -6.813082e+01
625 626 627 628 629 630 631 632
-1.034792e+02 -1.106195e+02 -1.383181e+02 -7.278672e+01 7.731929e+01 -4.985303e+01 -3.351860e+01 -1.446106e+02
633 634 635 636 637 638 639 640
-1.695208e+02 -1.196528e+02 -4.503259e+01 -1.332512e+02 -5.530082e+00 -2.278654e+02 1.143310e+02 4.228526e+01
641 642 643 644 645 646 647 648
1.425124e+02 -5.146886e+00 -2.192988e+01 -4.149881e+00 1.269350e+00 4.824935e+01 5.958372e+01 5.053674e+01
649 650 651 652 653 654 655 656
2.122112e+01 1.874032e+02 1.385034e+02 4.900414e+01 3.020137e+00 -1.968149e+01 8.418560e+01 1.033206e+02
657 658 659 660 661 662 663 664
1.249198e+02 -6.557172e+01 5.451802e-01 -6.808101e+01 5.380828e+01 -6.718815e+01 -4.581010e+00 -1.028661e+02
665 666 667 668 669 670 671 672
-7.074820e+00 -7.212744e+01 5.373839e+01 4.862819e+01 4.017677e+01 3.662266e+01 9.519024e+01 9.650310e+01
673 674 675 676 677 678 679 680
-6.407732e+01 -1.438551e+02 1.166747e+02 -2.142804e+01 -8.315275e+01 -1.188990e+02 -8.315186e+01 -4.179086e+01
681 682 683 684 685 686 687 688
-2.541045e+01 4.242996e+01 1.432698e+01 1.445290e+01 1.051498e+02 1.128454e+02 -3.079588e+01 -3.773136e+01
689 690 691 692 693 694 695 696
-6.646564e+00 -1.608576e+01 1.309791e+01 -1.977451e+01 -2.227165e+01 -1.165823e+02 -2.213930e+01 -7.775077e+01
697 698 699 700 701 702 703 704
-5.887204e+01 -1.047197e+02 -7.077654e+01 4.693425e+01 6.049914e+01 3.793863e+01 4.782572e+01 3.478742e+01
705 706 707 708 709 710 711 712
2.441090e+01 4.410988e+01 -5.243716e+01 -8.105495e+01 2.519899e+01 5.374105e+00 -2.725748e+01 5.813263e+01
713 714 715 716 717 718 719 720
5.673995e+01 1.355445e+01 -5.474153e+01 -6.294502e+01 -3.255968e+01 5.028360e-01 2.077962e+01 3.475748e+01
721 722 723 724 725 726 727 728
2.600174e+01 1.053896e+02 1.102056e+02 7.081253e+01 5.398014e+01 4.425154e+01 1.748111e+01 1.198300e+01
729 730 731 732 733 734 735 736
-6.122807e+01 -1.209392e+02 -3.140091e+01 -5.497276e+01 7.308011e+01 6.529808e+01 2.500463e+02 6.037421e+01
737 738 739 740 741 742 743 744
3.963548e+01 9.194688e+01 8.892872e+00 7.602297e+01 7.373783e-01 -3.157584e+01 -5.119954e+01 2.959791e+01
745 746 747 748 749 750 751 752
-1.878938e+01 -1.723897e+01 3.760871e+00 3.431364e+01 -4.100361e+01 -5.487014e+00 -2.411295e+00 -2.002220e+01
753 754 755 756 757 758 759 760
2.382353e+01 2.327254e+01 2.150210e+00 -1.108688e+02 1.038216e+02 9.764385e+00 -3.823196e+01 -2.363731e+01
761 762 763 764 765 766 767 768
-5.523014e+00 2.260199e+01 -7.690865e+00 1.111401e+02 1.144271e+01 -2.138782e+01 1.185238e+02 4.280349e+01
769 770 771 772 773 774 775 776
1.174851e+02 1.319449e+02 8.740961e+01 2.028926e+02 3.470376e+01 -3.765136e-01 2.265842e+01 1.242396e+02
777 778 779 780 781 782 783 784
1.659420e+02 1.877584e+02 6.590489e+01 3.914961e+01 -7.466426e+01 8.085835e+01 -6.036729e+00 2.025903e+02
785 786 787 788 789 790 791 792
6.615957e+01 8.516037e+01 1.539357e+02 -2.074165e+01 -4.391979e+01 7.100994e+01 -1.048085e+02 1.425349e+01
793 794 795 796 797 798 799 800
9.145853e+01 2.565363e+01 3.348566e+01 2.332080e+01 6.561234e+01 9.941802e+01 8.143705e+01 3.233158e+01
801 802 803 804 805 806 807 808
-5.958742e+01 1.348437e+02 -5.126123e+01 -2.526504e+01 -5.084379e+01 -1.397234e+02 -1.256504e+01 -2.101277e+01
809 810 811 812 813 814 815 816
1.269562e+02 5.392294e+01 1.475087e+02 1.394070e+02 2.542537e+02 -6.815111e+01 -2.819940e+01 -3.947898e+01
817 818 819 820 821 822 823 824
-2.401283e+01 -4.723047e+01 1.437852e+01 2.569215e+02 -2.407825e+01 -6.335409e+01 -3.295795e+01 -3.949582e+01
825 826 827 828 829 830 831 832
-2.743568e+01 1.166312e+02 2.616549e+01 1.144067e+02 2.331239e+00 7.538822e+00 1.958774e+02 6.354993e+01
833 834 835 836 837 838 839 840
5.397791e+01 1.261882e+01 1.018965e+02 2.644574e+02 -1.952863e+00 6.040305e+01 6.854998e+01 -2.535445e+01
841 842 843 844 845 846 847 848
-1.973286e+01 2.103305e+01 -5.110021e+00 6.332205e+01 5.203151e+01 1.067265e+02 1.264067e+02 1.402567e+01
849 850 851 852 853 854 855 856
-1.282414e+02 2.496160e+02 1.490624e+02 1.069952e+02 -1.261540e+01 1.298303e+02 1.158330e+02 2.210646e+02
857 858 859 860 861 862 863 864
7.903032e+01 1.132930e+02 7.647984e+01 9.123551e+01 1.017907e+01 1.266777e+01 -1.542462e+02 5.253126e+01
865 866 867 868 869 870 871 872
2.055355e+02 8.541026e+01 1.271907e+01 8.100776e-01 -8.199893e+01 1.720376e+01 1.031498e+02 6.126351e+01
873 874 875 876 877 878 879 880
8.256907e+01 7.447008e+01 -8.215890e+01 -3.627893e+01 -9.466236e+00 -5.967049e+01 -5.825762e+01 4.554007e+01
881 882 883 884 885 886 887 888
-5.587627e+01 -3.002733e+01 -3.174179e+01 -1.798018e+01 -3.258294e+00 4.285872e+01 -1.626038e+01 -1.275354e+01
889 890 891 892 893 894 895 896
-4.721001e+00 9.144891e+00 1.139872e+02 -9.108100e+01 1.524891e+00 -9.877582e+01 -1.651340e+02 -2.009109e+02
897 898 899 900 901 902 903 904
-4.621379e+01 -6.512082e+01 -6.887842e+01 -7.868673e+01 -9.443729e+01 -3.835089e+01 6.752552e+00 -4.988082e+01
905 906 907 908 909 910 911 912
-3.204842e+01 -7.676248e+01 -1.016340e+02 -2.059909e+02 -6.251049e+00 -8.984816e+00 -1.291572e+02 -1.333141e+02
913 914 915 916 917 918 919 920
-5.559783e+01 -5.982033e+00 -1.184034e+02 -2.736099e+01 -5.360703e+01 -3.195424e+01 -2.084964e+01 -3.992596e+01
921 922 923 924 925 926 927 928
2.740487e+02 2.710047e+02 5.365480e+01 1.028226e+02 2.501512e+02 -3.736606e+01 -4.901619e+01 -7.176977e+01
929 930 931 932 933 934 935 936
-4.402916e+01 -3.193480e+01 4.018559e+01 5.204913e+01 7.903307e+00 -1.967508e+00 -3.029764e+01 2.374423e+01
937 938 939 940 941 942 943 944
-2.994411e+00 3.807913e+01 3.697916e+01 6.474913e+01 -4.229577e+01 -6.593686e+01 -6.927039e+01 -1.115493e+02
945 946 947 948 949 950 951 952
-1.185011e+02 -7.318212e+01 4.374159e+01 -8.909625e+01 -1.036645e+02 -1.088486e+02 -1.081496e+02 -9.831617e+01
953 954 955 956 957 958 959 960
-1.317263e+02 -2.226445e+02 -9.074015e+01 -8.922170e+01 -1.040276e+02 -1.227691e+02 -9.712424e+01 1.896751e+01
961 962 963 964 965 966 967 968
-9.184942e+01 -1.229276e+02 -1.271502e+02 -8.747175e+01 -1.601248e+02 -1.159912e+02 -9.906025e+01 -6.741435e+01
969 970 971 972 973 974 975 976
-1.490844e+02 -9.932051e+01 1.637759e+01 7.278451e+01 9.779111e+01 6.643280e+01 4.556030e+01 5.969428e+01
977 978 979 980 981 982 983 984
-2.476180e+01 8.574712e+01 3.005770e+01 -1.178587e+01 7.892625e+01 1.736803e+01 -7.036608e+01 1.017248e+01
985 986 987 988 989 990 991 992
7.578803e+01 2.076549e+01 -2.210608e+01 3.362958e+01 7.716032e+00 2.203549e+01 -6.782608e+01 -2.445122e+02
993 994 995 996 997 998 999 1000
-8.399709e+01 -4.376110e+01 -2.013670e+02 -7.566329e+01 -1.199657e+02 1.390746e-01 -4.838757e+01 -8.821110e+01
[ reached 'max' / getOption("max.print") -- omitted 6121 entries ]plot(esoluc.lm)
Post-hoc comparisons
esoluc.emm <- emmeans(esoluc.lm, ~ begin_date_year*age_group)
pairs(esoluc.emm, simple = "age_group")begin_date_year = 1990:
contrast estimate SE df t.ratio p.value
age_group0 - age_group1 -127.21 9.58 7096 -13.277 <.0001
age_group0 - age_group2 -243.47 9.38 7096 -25.966 <.0001
age_group0 - age_group3 -318.87 9.36 7096 -34.072 <.0001
age_group0 - age_group4 -382.12 9.41 7096 -40.626 <.0001
age_group0 - age_group5 -436.90 9.49 7096 -46.045 <.0001
age_group0 - age_group6 -490.91 9.65 7096 -50.880 <.0001
age_group0 - age_group7 -537.66 10.00 7096 -53.700 <.0001
age_group0 - age_group8 -602.60 10.80 7096 -56.010 <.0001
age_group0 - age_group9 -678.24 12.70 7096 -53.285 <.0001
age_group0 - age_group10 -682.31 16.50 7096 -41.454 <.0001
age_group1 - age_group2 -116.26 3.80 7096 -30.591 <.0001
age_group1 - age_group3 -191.66 3.74 7096 -51.231 <.0001
age_group1 - age_group4 -254.91 3.84 7096 -66.407 <.0001
age_group1 - age_group5 -309.68 4.03 7096 -76.906 <.0001
age_group1 - age_group6 -363.70 4.38 7096 -83.027 <.0001
age_group1 - age_group7 -410.44 5.13 7096 -80.022 <.0001
age_group1 - age_group8 -475.38 6.45 7096 -73.721 <.0001
age_group1 - age_group9 -551.03 9.37 7096 -58.819 <.0001
age_group1 - age_group10 -555.09 14.00 7096 -39.546 <.0001
age_group2 - age_group3 -75.40 3.14 7096 -24.037 <.0001
age_group2 - age_group4 -138.65 3.25 7096 -42.666 <.0001
age_group2 - age_group5 -193.43 3.47 7096 -55.777 <.0001
age_group2 - age_group6 -247.44 3.87 7096 -63.911 <.0001
age_group2 - age_group7 -294.19 4.70 7096 -62.580 <.0001
age_group2 - age_group8 -359.13 6.11 7096 -58.764 <.0001
age_group2 - age_group9 -434.77 9.14 7096 -47.578 <.0001
age_group2 - age_group10 -438.83 13.90 7096 -31.605 <.0001
age_group3 - age_group4 -63.25 3.18 7096 -19.922 <.0001
age_group3 - age_group5 -118.02 3.40 7096 -34.735 <.0001
age_group3 - age_group6 -172.04 3.81 7096 -45.177 <.0001
age_group3 - age_group7 -218.79 4.65 7096 -47.060 <.0001
age_group3 - age_group8 -283.73 6.07 7096 -46.739 <.0001
age_group3 - age_group9 -359.37 9.11 7096 -39.444 <.0001
age_group3 - age_group10 -363.43 13.90 7096 -26.207 <.0001
age_group4 - age_group5 -54.77 3.50 7096 -15.656 <.0001
age_group4 - age_group6 -108.79 3.90 7096 -27.914 <.0001
age_group4 - age_group7 -155.53 4.72 7096 -32.937 <.0001
age_group4 - age_group8 -220.47 6.13 7096 -35.995 <.0001
age_group4 - age_group9 -296.11 9.15 7096 -32.373 <.0001
age_group4 - age_group10 -300.18 13.90 7096 -21.607 <.0001
age_group5 - age_group6 -54.02 4.08 7096 -13.244 <.0001
age_group5 - age_group7 -100.76 4.87 7096 -20.682 <.0001
age_group5 - age_group8 -165.70 6.24 7096 -26.552 <.0001
age_group5 - age_group9 -241.34 9.22 7096 -26.165 <.0001
age_group5 - age_group10 -245.41 13.90 7096 -17.600 <.0001
age_group6 - age_group7 -46.74 5.17 7096 -9.050 <.0001
age_group6 - age_group8 -111.69 6.47 7096 -17.259 <.0001
age_group6 - age_group9 -187.33 9.38 7096 -19.968 <.0001
age_group6 - age_group10 -191.39 14.00 7096 -13.623 <.0001
age_group7 - age_group8 -64.94 7.00 7096 -9.280 <.0001
age_group7 - age_group9 -140.58 9.75 7096 -14.417 <.0001
age_group7 - age_group10 -144.65 14.30 7096 -10.116 <.0001
age_group8 - age_group9 -75.64 10.50 7096 -7.204 <.0001
age_group8 - age_group10 -79.71 14.80 7096 -5.377 <.0001
age_group9 - age_group10 -4.07 16.30 7096 -0.249 1.0000
P value adjustment: tukey method for comparing a family of 11 estimates test(pairs(esoluc.emm, by = "begin_date_year"), by = NULL, adjust = "mvt") contrast begin_date_year estimate SE df t.ratio p.value
age_group0 - age_group1 1990 -127.21 9.58 7096 -13.277 <.0001
age_group0 - age_group2 1990 -243.47 9.38 7096 -25.966 <.0001
age_group0 - age_group3 1990 -318.87 9.36 7096 -34.072 <.0001
age_group0 - age_group4 1990 -382.12 9.41 7096 -40.626 <.0001
age_group0 - age_group5 1990 -436.90 9.49 7096 -46.045 <.0001
age_group0 - age_group6 1990 -490.91 9.65 7096 -50.880 <.0001
age_group0 - age_group7 1990 -537.66 10.00 7096 -53.700 <.0001
age_group0 - age_group8 1990 -602.60 10.80 7096 -56.010 <.0001
age_group0 - age_group9 1990 -678.24 12.70 7096 -53.285 <.0001
age_group0 - age_group10 1990 -682.31 16.50 7096 -41.454 <.0001
age_group1 - age_group2 1990 -116.26 3.80 7096 -30.591 <.0001
age_group1 - age_group3 1990 -191.66 3.74 7096 -51.231 <.0001
age_group1 - age_group4 1990 -254.91 3.84 7096 -66.407 <.0001
age_group1 - age_group5 1990 -309.68 4.03 7096 -76.906 <.0001
age_group1 - age_group6 1990 -363.70 4.38 7096 -83.027 <.0001
age_group1 - age_group7 1990 -410.44 5.13 7096 -80.022 <.0001
age_group1 - age_group8 1990 -475.38 6.45 7096 -73.721 <.0001
age_group1 - age_group9 1990 -551.03 9.37 7096 -58.819 <.0001
age_group1 - age_group10 1990 -555.09 14.00 7096 -39.546 <.0001
age_group2 - age_group3 1990 -75.40 3.14 7096 -24.037 <.0001
age_group2 - age_group4 1990 -138.65 3.25 7096 -42.666 <.0001
age_group2 - age_group5 1990 -193.43 3.47 7096 -55.777 <.0001
age_group2 - age_group6 1990 -247.44 3.87 7096 -63.911 <.0001
age_group2 - age_group7 1990 -294.19 4.70 7096 -62.580 <.0001
age_group2 - age_group8 1990 -359.13 6.11 7096 -58.764 <.0001
age_group2 - age_group9 1990 -434.77 9.14 7096 -47.578 <.0001
age_group2 - age_group10 1990 -438.83 13.90 7096 -31.605 <.0001
age_group3 - age_group4 1990 -63.25 3.18 7096 -19.922 <.0001
age_group3 - age_group5 1990 -118.02 3.40 7096 -34.735 <.0001
age_group3 - age_group6 1990 -172.04 3.81 7096 -45.177 <.0001
age_group3 - age_group7 1990 -218.79 4.65 7096 -47.060 <.0001
age_group3 - age_group8 1990 -283.73 6.07 7096 -46.739 <.0001
age_group3 - age_group9 1990 -359.37 9.11 7096 -39.444 <.0001
age_group3 - age_group10 1990 -363.43 13.90 7096 -26.207 <.0001
age_group4 - age_group5 1990 -54.77 3.50 7096 -15.656 <.0001
age_group4 - age_group6 1990 -108.79 3.90 7096 -27.914 <.0001
age_group4 - age_group7 1990 -155.53 4.72 7096 -32.937 <.0001
age_group4 - age_group8 1990 -220.47 6.13 7096 -35.995 <.0001
age_group4 - age_group9 1990 -296.11 9.15 7096 -32.373 <.0001
age_group4 - age_group10 1990 -300.18 13.90 7096 -21.607 <.0001
age_group5 - age_group6 1990 -54.02 4.08 7096 -13.244 <.0001
age_group5 - age_group7 1990 -100.76 4.87 7096 -20.682 <.0001
age_group5 - age_group8 1990 -165.70 6.24 7096 -26.552 <.0001
age_group5 - age_group9 1990 -241.34 9.22 7096 -26.165 <.0001
age_group5 - age_group10 1990 -245.41 13.90 7096 -17.600 <.0001
age_group6 - age_group7 1990 -46.74 5.17 7096 -9.050 <.0001
age_group6 - age_group8 1990 -111.69 6.47 7096 -17.259 <.0001
age_group6 - age_group9 1990 -187.33 9.38 7096 -19.968 <.0001
age_group6 - age_group10 1990 -191.39 14.00 7096 -13.623 <.0001
age_group7 - age_group8 1990 -64.94 7.00 7096 -9.280 <.0001
age_group7 - age_group9 1990 -140.58 9.75 7096 -14.417 <.0001
age_group7 - age_group10 1990 -144.65 14.30 7096 -10.116 <.0001
age_group8 - age_group9 1990 -75.64 10.50 7096 -7.204 <.0001
age_group8 - age_group10 1990 -79.71 14.80 7096 -5.377 <.0001
age_group9 - age_group10 1990 -4.07 16.30 7096 -0.249 1.0000
P value adjustment: mvt method for 55 tests #export tables
# #interpret(eta_squared(esoluc.lm), rules = "cohen1992") %>%
# write.csv(file = "Outputs/Tables/esoluc_eta_squared.csv")
#slope contrasts
# Obtain slope estimates
esoluc.slopes <- emtrends(esoluc.lm, ~age_group, var = "begin_date_year")
# Compare estimated slopes to 0
esoluc.slope.contrasts <- test(esoluc.slopes) %>%
mutate(Species = "Northern Pike") %>%
rename(Age = age_group)
esoluc.slope.contrasts %>%
write.csv(file = "Outputs/Tables/esoluc_emmeans.csv")Plot raw data
(esoluc.length.year.plot <- ggplot(data = esoluc %>%
mutate(Age = as.factor(age_group)),
aes(x = begin_date_year, y = length_mean_mm,
color = Age, fill = Age, group = Age))+
#geom_point()+
geom_smooth(method = "lm")+
stat_poly_eq(use_label("eq"))+
scale_color_viridis_d()+
scale_fill_viridis_d()+
labs(y = "Length (mm)", x = "Year")+
theme_bw()
)`geom_smooth()` using formula = 'y ~ x'
Marginal Effects
result <- predict_response(esoluc.lm, c("begin_date_year", "age_group"))
# plot(result)
# test_predictions(result) %>%
# write.csv(file = "Outputs/Tables/esoluc_pairwise_length_time_slopes.csv", row.names = F)
(esoluc.marginal.plot <- ggpredict(esoluc.lm, terms = c("begin_date_year", "age_group")) %>%
plot()+
#stat_poly_eq(use_label("eq"))+
stat_regline_equation()+
stat_poly_line()+
# annotate(geom = "text", label = "y = 1.3E4 - 5.5x", x = 1950, y = 1300)+
# annotate(geom = "text", label = "y = 9.1E3 - 4.1x", x = 1950, y = 1150)+
# annotate(geom = "text", label = "y = 8.9E3 - 4.1x", x = 1950, y = 1050)+
# annotate(geom = "text", label = "y = 6.5E3 - 2.9x", x = 1950, y = 925)+
# annotate(geom = "text", label = "y = 5.2E3 - 2.2x", x = 1950, y = 850)+
# annotate(geom = "text", label = "y = 3.4E3 - 1.4x", x = 1950, y = 750)+
# annotate(geom = "text", label = "y = 2.6E3 - 1x", x = 1950, y = 680)+
# annotate(geom = "text", label = "y = 1.8E3 - 0.64x", x = 1950, y = 615)+
# annotate(geom = "text", label = "y = 2.4E3 - 0.99x", x = 1950, y = 550)+
# annotate(geom = "text", label = "y = 3.2E3 - 1.4x", x = 1950, y = 460)+
# annotate(geom = "text", label = "y = 4.3E3 - 2.1x", x = 1950, y = 375)+
scale_color_viridis_d()+
scale_fill_viridis_d()+
labs(title = NULL,
y = "Mean Length-at-Age (mm)",
x = "Year")+
guides(fill = guide_legend(title = "Age", reverse = T), color = guide_legend(title = "Age", reverse = T))+
theme_bw()
)Scale for colour is already present.
Adding another scale for colour, which will replace the existing scale.ggsave(filename = "~/GitHub/EAGER_growth/Outputs/Figures/Linear Models/esoluc_marginal_effects_plot.tiff",
esoluc.marginal.plot,
dpi = 300, width = 200, height = 150, units = "mm")
Pumpkinseed Sunfish
Model
#filter data for only Black Crappie, Pomooxis nigromaculatus
lepgib <- all.grow.merge %>% filter(species == "pumpkinseed_sunfish") %>%
filter(age_group %in% c(1, 2, 3, 4, 5, 6, 7, 8, 9, 10), #match age groups in BRTs
!is.na(age_group), !is.na(begin_date_year), !is.na(length_mean_mm),
!is.na(log_max_depth), !is.na(logarea))
#linear model
lepgib.lm <- lm(length_mean_mm ~ begin_date_year*age_group + log_max_depth + logarea + doy, data = lepgib)
summary(lepgib.lm)
Call:
lm(formula = length_mean_mm ~ begin_date_year * age_group + log_max_depth +
logarea + doy, data = lepgib)
Residuals:
Min 1Q Median 3Q Max
-80.82 -14.39 -0.49 13.69 597.58
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 1.246e+03 1.285e+02 9.698 < 2e-16 ***
begin_date_year -5.950e-01 6.452e-02 -9.222 < 2e-16 ***
age_group2 -5.104e+02 1.493e+02 -3.418 0.000634 ***
age_group3 -9.978e+02 1.442e+02 -6.922 4.87e-12 ***
age_group4 -1.310e+03 1.458e+02 -8.989 < 2e-16 ***
age_group5 -1.465e+03 1.508e+02 -9.714 < 2e-16 ***
age_group6 -1.482e+03 1.625e+02 -9.117 < 2e-16 ***
age_group7 -1.615e+03 1.844e+02 -8.758 < 2e-16 ***
age_group8 -1.640e+03 2.488e+02 -6.594 4.60e-11 ***
age_group9 -1.247e+03 3.283e+02 -3.798 0.000147 ***
age_group10 -1.516e+03 5.598e+02 -2.707 0.006801 **
log_max_depth -1.107e+00 4.146e-01 -2.670 0.007598 **
logarea 1.928e+00 2.204e-01 8.749 < 2e-16 ***
doy 3.925e-02 6.534e-03 6.007 1.99e-09 ***
begin_date_year:age_group2 2.707e-01 7.508e-02 3.606 0.000313 ***
begin_date_year:age_group3 5.287e-01 7.247e-02 7.296 3.31e-13 ***
begin_date_year:age_group4 6.965e-01 7.327e-02 9.505 < 2e-16 ***
begin_date_year:age_group5 7.826e-01 7.579e-02 10.326 < 2e-16 ***
begin_date_year:age_group6 7.970e-01 8.161e-02 9.767 < 2e-16 ***
begin_date_year:age_group7 8.693e-01 9.255e-02 9.393 < 2e-16 ***
begin_date_year:age_group8 8.881e-01 1.246e-01 7.128 1.12e-12 ***
begin_date_year:age_group9 6.951e-01 1.643e-01 4.230 2.37e-05 ***
begin_date_year:age_group10 8.328e-01 2.799e-01 2.976 0.002935 **
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 22.86 on 6610 degrees of freedom
(54 observations deleted due to missingness)
Multiple R-squared: 0.6946, Adjusted R-squared: 0.6936
F-statistic: 683.4 on 22 and 6610 DF, p-value: < 2.2e-16#calculate and interpret effect sizes
eta_squared(lepgib.lm) begin_date_year age_group log_max_depth logarea
0.010860387 0.667589003 0.000213458 0.002978306
doy begin_date_year:age_group
0.002634652 0.010339550 #interpret(eta_squared(lepgib.lm), rules = "cohen1992")
#calculate AIC score
AIC(lepgib.lm)[1] 60360.85#examine model fit
testDispersion(lepgib.lm)
DHARMa nonparametric dispersion test via sd of residuals fitted vs. simulated
data: simulationOutput
dispersion = 0.9975, p-value = 0.928
alternative hypothesis: two.sided
simulation.output <- simulateResiduals(fittedModel = lepgib.lm)
residuals(lepgib.lm) 1 2 3 4 5 6 7 8 9
4.22419131 55.54215226 47.57597800 18.42718756 77.04684610 -17.82794076 -7.00647259 -10.60475526 -8.07371855
10 11 12 13 14 15 16 17 18
-10.88703132 -8.62421877 60.94558044 -24.69835903 -37.96679017 -10.34427555 -33.77270669 -10.62038396 -1.91685873
19 20 21 22 23 24 25 26 27
-13.90613115 -19.59593925 -56.57103706 25.61178539 25.93445435 16.22753598 6.35338360 0.64700045 -7.40066750
28 29 30 31 32 33 34 35 36
-18.15033026 19.81828568 21.08828568 10.88903583 22.36150019 11.59484992 8.31404060 15.50350019 26.15751658
37 38 39 40 41 42 43 44 45
7.46737394 12.57986883 23.44818325 -23.85929273 -14.68050187 -14.86598688 -11.26827461 0.08207162 -43.43848342
46 47 48 49 50 51 52 53 54
4.36949813 15.12500641 -21.38117388 -25.97189424 -37.96880296 -42.71680096 -35.39042374 -56.99914409 -29.20938415
55 56 57 58 59 60 61 62 63
-16.88063600 -7.97001626 -25.92527477 -35.70442255 -52.23314291 42.47114959 46.63537878 44.16448293 35.46652937
64 65 66 67 68 69 70 71 72
17.38237483 9.38718950 21.60135740 42.82537878 -30.08065649 -16.95978351 0.66105905 -19.49978351 1.96250518
73 74 75 76 77 78 79 80 81
11.60741110 -23.11989653 9.06741110 40.63093632 27.36250518 -6.17258890 -2.54906368 -20.89749482 -20.57989653
82 83 84 85 86 87 88 89 90
10.41488887 -10.39623935 -17.38577404 -8.95304448 18.03488887 -17.01838221 0.25488887 -10.92860004 -15.11942535
91 92 93 94 95 96 97 98 99
-26.33195472 6.64278642 -16.93371106 -25.90838846 -39.03195472 -28.55435643 -39.24942535 19.38804528 9.54564357
100 101 102 103 104 105 106 107 108
-17.01838846 -34.46704880 -47.35352358 -41.25435643 -1.03030818 2.67830407 -11.80796147 -16.17556296 8.72567126
109 110 111 112 113 114 115 116 117
-7.48169593 8.77696788 9.12969182 0.89203853 1.60443704 -0.93308385 1.10567126 19.98547351 2.30706716
118 119 120 121 122 123 124 125 126
-15.33357555 -60.88382807 -14.09244016 -4.42683882 4.44697415 2.56491728 7.62619692 11.41577305 5.18995687
127 128 129 130 131 132 133 134 135
-16.04942850 9.46503526 30.70886100 33.73340390 12.85170193 26.47552767 -0.97992944 11.24303526 23.08886100
136 137 138 139 140 141 142 143 144
6.21673723 22.16503526 21.39552767 -8.13433371 -7.26645911 10.35424974 33.18538514 46.26904895 29.07275233
145 146 147 148 149 150 151 152 153
28.63010623 18.14399722 36.74404895 34.15275233 32.01677290 -8.20049814 -6.97460096 2.74222485 -15.47409243
154 155 156 157 158 159 160 161 162
-14.59460096 -14.59460096 -20.55409243 -16.45599821 -13.62678975 -14.77129391 17.24671599 27.58239402 17.02442899
163 164 165 166 167 168 169 170 171
-17.01345641 -11.46929391 13.42022249 23.71191783 -20.68234530 4.61737275 23.94307963 -32.53093932 12.93641296
172 173 174 175 176 177 178 179 180
33.50906068 -19.72278975 -31.28129391 66.52906068 -16.64496980 1.76128635 3.64608622 6.98988703 21.88221487
181 182 183 184 185 186 187 188 189
13.64451794 -6.48496980 -21.09871365 -9.75946934 13.19877591 15.95554820 6.84128635 44.85053066 22.34277591
190 191 192 193 194 195 196 197 198
32.46554820 -3.57280345 -9.43355578 -13.84073256 -14.88096211 -17.94430782 -9.53749625 -16.99851774 -5.62355578
199 200 201 202 203 204 205 206 207
-5.44667639 -3.33930782 -15.24761827 -6.02057317 -6.83851774 -2.70430782 0.78148226 -19.52178556 -17.06724609
208 209 210 211 212 213 214 215 216
4.05180815 -16.92613498 25.92586147 13.55723317 28.12083424 36.88938764 43.68684418 24.83387528 27.15661162
217 218 219 220 221 222 223 224 225
10.79655474 61.09559233 46.18759433 -11.10362343 24.18175011 57.36973117 41.96940916 42.14297031 26.91509947
226 227 228 229 230 231 232 233 234
26.63708345 59.74940916 49.76297031 -18.97611926 -25.48450578 4.87775011 43.39973117 34.34940916 37.90963698
235 236 237 238 239 240 241 242 243
-26.85331669 29.14493947 14.32218377 28.39042903 -10.34331669 17.71493947 36.81932663 23.31042903 3.19696345
244 245 246 247 248 249 250 251 252
11.02627280 11.14718377 20.77042903 -7.54429564 -12.90908611 -4.04487104 -2.94322828 -7.49478995 5.40228735
253 254 255 256 257 258 259 260 261
3.11478318 -5.92284327 -14.94504404 -13.02718422 -24.27149392 -24.05293481 -42.52965835 -11.28062076 -15.30290448
262 263 264 265 266 267 268 269 270
-10.44096635 -17.70293481 -69.21837871 46.85195872 10.93772953 20.76120872 37.60772953 -0.49227047 -1.14689070
271 272 273 274 275 276 277 278 279
-1.03238204 16.44620262 8.65208631 17.88366837 20.44935271 -12.02197129 11.13927864 23.43120262 3.36041965
280 281 282 283 284 285 286 287 288
25.66241837 16.95685271 23.53802871 3.99541965 10.60685271 -43.31215042 -20.44009425 -4.99772568 7.21908589
289 290 291 292 293 294 295 296 297
-15.96030698 -27.43715042 -26.09736698 -20.66105901 -41.67103613 -28.48026268 -14.21193560 -24.00364031 -33.15215042
298 299 300 301 302 303 304 305 306
-2.03439235 -16.27103613 -11.44461296 10.68963135 8.67120994 17.35464889 6.07505460 5.70038704 8.87534564
307 308 309 310 311 312 313 314 315
20.94787660 25.99064889 9.96361863 -2.22682499 -8.05558897 -11.43586911 -15.63561296 12.50361863 32.21376544
316 317 318 319 320 321 322 323 324
7.89729066 27.90193240 23.69379096 4.71035982 15.18795810 52.56328348 21.55193240 17.97879096 -8.46121149
325 326 327 328 329 330 331 332 333
1.16743744 4.14622332 25.70960838 12.53497843 15.47899899 15.37761124 7.17964132 -18.24697362 -8.13827024
334 335 336 337 338 339 340 341 342
14.70241699 7.40436257 -16.52702535 0.80302638 -19.56827024 -14.84784628 -14.14880607 -6.48782502 -9.90781369
343 344 345 346 347 348 349 350 351
-2.45291921 -0.32412338 -7.37547274 8.55374746 -14.99547274 -35.90949168 8.56587662 3.60434078 5.74828781
352 353 354 355 356 357 358 359 360
-3.30259728 14.97781116 15.12684522 0.81934448 22.38128836 17.43142713 4.72060136 -0.84925280 -1.85926937
361 362 363 364 365 366 367 368 369
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379 380 381 382 383 384 385 386 387
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397 398 399 400 401 402 403 404 405
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415 416 417 418 419 420 421 422 423
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433 434 435 436 437 438 439 440 441
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442 443 444 445 446 447 448 449 450
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451 452 453 454 455 456 457 458 459
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523 524 525 526 527 528 529 530 531
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532 533 534 535 536 537 538 539 540
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541 542 543 544 545 546 547 548 549
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550 551 552 553 554 555 556 557 558
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559 560 561 562 563 564 565 566 567
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568 569 570 571 572 573 574 575 576
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577 578 579 580 581 582 583 584 585
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586 587 588 589 590 591 592 593 594
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595 596 597 598 599 600 601 602 603
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604 605 606 607 608 609 610 611 612
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613 614 615 616 617 618 619 620 621
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622 623 624 625 626 627 628 629 630
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631 632 633 634 635 636 637 638 639
24.80779240 0.95217954 -3.60972995 -17.48625604 107.60452883 15.61590191 4.91112573 217.69884621 7.18527005
640 641 642 643 644 645 646 647 648
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649 650 651 652 653 654 655 656 657
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658 659 660 661 662 663 664 665 666
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667 668 669 670 671 672 673 674 675
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676 677 678 679 680 681 682 683 684
14.84130768 30.44960296 14.95109285 3.53314902 -18.06890715 29.17960296 32.73109285 11.35551759 14.68232916
685 686 687 688 689 690 691 692 693
29.60293630 -28.21685098 -14.04448241 -29.31602672 11.95475814 39.20502012 32.02802172 26.46904386 28.05724234
694 695 696 697 698 699 700 701 702
9.80302172 17.06240886 22.11475814 17.91665850 8.56809148 48.67150075 32.54960039 24.87486236 3.97881553
703 704 705 706 707 708 709 710 711
51.21150075 33.81960039 25.50986236 14.52286396 17.88986236 20.73150075 41.43960039 -36.21670776 10.46450800
712 713 714 715 716 717 718 719 720
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721 722 723 724 725 726 727 728 729
60.85258107 36.36849967 15.52651606 36.66449760 36.48151606 31.29570675 35.84651606 23.67570675 21.42449760
730 731 732 733 734 735 736 737 738
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739 740 741 742 743 744 745 746 747
4.43952218 -13.15668774 -35.30972578 -54.19490256 49.94775660 35.37362147 19.18040995 -20.49945393 -15.46562838
748 749 750 751 752 753 754 755 756
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757 758 759 760 761 762 763 764 765
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766 767 768 769 770 771 772 773 774
9.44440247 -15.19902409 -8.33559753 22.47296299 22.01447938 7.55767007 6.26940247 15.59379632 9.56847938
775 776 777 778 779 780 781 782 783
3.24506148 -12.03418837 -29.66515431 -56.73120728 2.43226586 -23.91134222 -9.53343822 -33.51440416 3.16656178
784 785 786 787 788 789 790 791 792
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793 794 795 796 797 798 799 800 801
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802 803 804 805 806 807 808 809 810
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811 812 813 814 815 816 817 818 819
36.16500510 26.97040623 26.83613807 40.11560644 -1.35346126 -8.75386382 -43.17524787 -23.36679459 1.19155603
820 821 822 823 824 825 826 827 828
20.56794795 9.54585195 6.73155268 4.21883304 15.99523876 14.17869810 9.50660210 3.94063616 2.90958319
829 830 831 832 833 834 835 836 837
4.13548038 4.09880944 8.23520136 -11.67689464 -27.32479864 3.15623783 -3.42605529 8.72610758 8.26560376
838 839 840 841 842 843 844 845 846
13.64616491 1.59329406 3.31650166 -5.58250773 41.96454311 16.51606449 3.00322419 -8.09361826 -1.01709036
847 848 849 850 851 852 853 854 855
26.72454311 6.03856449 6.10766863 16.76290964 -8.34962479 -6.29545689 -5.64543551 13.91971507 -4.79362479
856 857 858 859 860 861 862 863 864
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865 866 867 868 869 870 871 872 873
4.38684418 27.44768507 2.19283091 13.45948101 0.89809486 -0.39996126 7.60417751 14.11268507 -2.15944885
874 875 876 877 878 879 880 881 882
29.56249503 19.10930047 18.39291914 10.14195387 7.01527063 -8.20225238 20.80263380 28.83514136 17.76195387
883 884 885 886 887 888 889 890 891
2.78193730 -11.92417164 13.18263380 14.91614709 14.41490220 -39.90285025 -39.90285025 27.07565234 -15.38367705
892 893 894 895 896 897 898 899 900
6.49217058 16.49507534 10.26200638 -14.86737019 3.79507534 -15.13799362 5.18200638 -3.19179047 43.11035135
901 902 903 904 905 906 907 908 909
4.64359310 10.47160950 4.65080018 13.14996174 21.25169648 0.30775699 12.16829740 -16.09173008 5.50369648
910 911 912 913 914 915 916 917 918
-13.66224301 27.32412063 25.82077338 -4.76503593 26.33169648 21.47442366 -11.64422662 13.01496407 18.38952672
919 920 921 922 923 924 925 926 927
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928 929 930 931 932 933 934 935 936
0.73878031 -10.33041027 -24.06400916 -22.86968914 -9.10371969 -8.42541027 5.81878031 -14.62623530 -32.04775424
937 938 939 940 941 942 943 944 945
-41.41557625 -44.43983253 -9.48332497 -9.80384579 -0.57689267 4.87194795 7.76618646 3.42395503 -30.09197130
946 947 948 949 950 951 952 953 954
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955 956 957 958 959 960 961 962 963
45.34717587 15.12756239 38.17838998 6.01106286 27.74584268 28.22722774 15.77831207 17.99661835 16.62523059
964 965 966 967 968 969 970 971 972
-9.84863644 -23.98590480 30.79384268 36.69389441 35.37259779 53.98843395 46.65557389 46.04773992 40.59202085
973 974 975 976 977 978 979 980 981
2.51840098 -25.50009872 -12.77371219 -7.71243255 -33.59467460 -17.38767260 -12.33805797 -14.32992445 15.06906775
982 983 984 985 986 987 988 989 990
3.15682570 22.53882769 -9.01414121 -19.16508580 -21.50896086 -30.07901455 -14.89792327 -31.07592127 22.90218382
991 992 993 994 995 996 997 998 999
4.07892813 -4.39532662 -2.49781618 12.65142813 -12.65032662 -35.29355433 15.28218382 4.38231406 -1.51916071
1000
-1.76999357
[ reached 'max' / getOption("max.print") -- omitted 5633 entries ]residuals(lepgib.lm, quantileFunction = qnorm) 1 2 3 4 5 6 7 8 9
4.22419131 55.54215226 47.57597800 18.42718756 77.04684610 -17.82794076 -7.00647259 -10.60475526 -8.07371855
10 11 12 13 14 15 16 17 18
-10.88703132 -8.62421877 60.94558044 -24.69835903 -37.96679017 -10.34427555 -33.77270669 -10.62038396 -1.91685873
19 20 21 22 23 24 25 26 27
-13.90613115 -19.59593925 -56.57103706 25.61178539 25.93445435 16.22753598 6.35338360 0.64700045 -7.40066750
28 29 30 31 32 33 34 35 36
-18.15033026 19.81828568 21.08828568 10.88903583 22.36150019 11.59484992 8.31404060 15.50350019 26.15751658
37 38 39 40 41 42 43 44 45
7.46737394 12.57986883 23.44818325 -23.85929273 -14.68050187 -14.86598688 -11.26827461 0.08207162 -43.43848342
46 47 48 49 50 51 52 53 54
4.36949813 15.12500641 -21.38117388 -25.97189424 -37.96880296 -42.71680096 -35.39042374 -56.99914409 -29.20938415
55 56 57 58 59 60 61 62 63
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64 65 66 67 68 69 70 71 72
17.38237483 9.38718950 21.60135740 42.82537878 -30.08065649 -16.95978351 0.66105905 -19.49978351 1.96250518
73 74 75 76 77 78 79 80 81
11.60741110 -23.11989653 9.06741110 40.63093632 27.36250518 -6.17258890 -2.54906368 -20.89749482 -20.57989653
82 83 84 85 86 87 88 89 90
10.41488887 -10.39623935 -17.38577404 -8.95304448 18.03488887 -17.01838221 0.25488887 -10.92860004 -15.11942535
91 92 93 94 95 96 97 98 99
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100 101 102 103 104 105 106 107 108
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109 110 111 112 113 114 115 116 117
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118 119 120 121 122 123 124 125 126
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127 128 129 130 131 132 133 134 135
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136 137 138 139 140 141 142 143 144
6.21673723 22.16503526 21.39552767 -8.13433371 -7.26645911 10.35424974 33.18538514 46.26904895 29.07275233
145 146 147 148 149 150 151 152 153
28.63010623 18.14399722 36.74404895 34.15275233 32.01677290 -8.20049814 -6.97460096 2.74222485 -15.47409243
154 155 156 157 158 159 160 161 162
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163 164 165 166 167 168 169 170 171
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172 173 174 175 176 177 178 179 180
33.50906068 -19.72278975 -31.28129391 66.52906068 -16.64496980 1.76128635 3.64608622 6.98988703 21.88221487
181 182 183 184 185 186 187 188 189
13.64451794 -6.48496980 -21.09871365 -9.75946934 13.19877591 15.95554820 6.84128635 44.85053066 22.34277591
190 191 192 193 194 195 196 197 198
32.46554820 -3.57280345 -9.43355578 -13.84073256 -14.88096211 -17.94430782 -9.53749625 -16.99851774 -5.62355578
199 200 201 202 203 204 205 206 207
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208 209 210 211 212 213 214 215 216
4.05180815 -16.92613498 25.92586147 13.55723317 28.12083424 36.88938764 43.68684418 24.83387528 27.15661162
217 218 219 220 221 222 223 224 225
10.79655474 61.09559233 46.18759433 -11.10362343 24.18175011 57.36973117 41.96940916 42.14297031 26.91509947
226 227 228 229 230 231 232 233 234
26.63708345 59.74940916 49.76297031 -18.97611926 -25.48450578 4.87775011 43.39973117 34.34940916 37.90963698
235 236 237 238 239 240 241 242 243
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244 245 246 247 248 249 250 251 252
11.02627280 11.14718377 20.77042903 -7.54429564 -12.90908611 -4.04487104 -2.94322828 -7.49478995 5.40228735
253 254 255 256 257 258 259 260 261
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262 263 264 265 266 267 268 269 270
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271 272 273 274 275 276 277 278 279
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280 281 282 283 284 285 286 287 288
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289 290 291 292 293 294 295 296 297
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298 299 300 301 302 303 304 305 306
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307 308 309 310 311 312 313 314 315
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316 317 318 319 320 321 322 323 324
7.89729066 27.90193240 23.69379096 4.71035982 15.18795810 52.56328348 21.55193240 17.97879096 -8.46121149
325 326 327 328 329 330 331 332 333
1.16743744 4.14622332 25.70960838 12.53497843 15.47899899 15.37761124 7.17964132 -18.24697362 -8.13827024
334 335 336 337 338 339 340 341 342
14.70241699 7.40436257 -16.52702535 0.80302638 -19.56827024 -14.84784628 -14.14880607 -6.48782502 -9.90781369
343 344 345 346 347 348 349 350 351
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352 353 354 355 356 357 358 359 360
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361 362 363 364 365 366 367 368 369
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370 371 372 373 374 375 376 377 378
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379 380 381 382 383 384 385 386 387
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388 389 390 391 392 393 394 395 396
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397 398 399 400 401 402 403 404 405
-20.39447286 -22.68814080 -26.48303980 -5.50853046 -10.28728053 -11.02894122 -25.78009864 -14.15276318 -15.41675333
406 407 408 409 410 411 412 413 414
-19.11373693 -9.87597482 -19.98908872 5.15461531 -24.72191366 12.14944262 6.74975132 12.66481919 -1.05960110
415 416 417 418 419 420 421 422 423
6.93392902 5.23685684 -29.26987814 -32.30055738 5.62086243 16.47481919 14.18039890 17.09392902 4.94105127
424 425 426 427 428 429 430 431 432
-14.52055738 -4.81024786 -9.71381488 -6.68426719 -8.47888464 -5.09221798 2.14373359 -2.43958119 -5.09221798
433 434 435 436 437 438 439 440 441
-1.34876641 35.58486348 25.64081239 33.98976601 36.81561363 41.01523048 31.95156253 16.97605306 11.73460523
442 443 444 445 446 447 448 449 450
-6.11952769 -45.09946466 0.73086975 18.08460523 -2.30952769 -22.44156283 -15.77913025 2.84460523 -0.85156283
451 452 453 454 455 456 457 458 459
-11.23279800 -4.34913025 -28.13827920 1.71778131 12.62579770 32.94579770 12.62579770 29.93435667 40.33688821
460 461 462 463 464 465 466 467 468
11.86701002 44.57714614 39.45097169 50.17585697 50.84172184 54.96851032 -19.00135357 32.64536082 52.36122569
469 470 471 472 473 474 475 476 477
52.32197584 25.45100529 -15.82291347 -20.72552828 -42.02269301 5.09175544 -2.89036833 1.54262354 -44.30649027
478 479 480 481 482 483 484 485 486
-6.99194737 -29.68521305 -24.17651927 -17.74618722 -14.17630492 -28.06608407 -18.20327664 -16.23627504 -10.08081097
487 488 489 490 491 492 493 494 495
-12.39579739 2.15889072 -29.88372703 -32.27180319 -31.76976369 -23.95597913 -33.09192727 -18.40575867 -7.14728615
496 497 498 499 500 501 502 503 504
-4.30317326 -4.94966776 -5.17334255 3.51459786 -18.55754399 -13.83966776 -10.43477113 -13.26479608 6.38592244
505 506 507 508 509 510 511 512 513
9.30423780 -9.82588542 -23.03636902 -34.43546733 -25.42356711 -15.93667263 -15.83987680 -33.67466921 -9.90705411
514 515 516 517 518 519 520 521 522
-26.66775482 -18.96164329 -10.19926767 -10.58730576 -4.80857410 -10.26097693 -43.53987859 -18.01911650 -33.29838475
523 524 525 526 527 528 529 530 531
-30.19741615 -30.98287326 3.92888555 31.44326555 18.52866769 10.53820144 4.88416630 2.65802730 14.84859889
532 533 534 535 536 537 538 539 540
10.16626447 -3.94740111 -5.38966564 16.62659889 21.39416630 3.65684391 -7.18945271 1.46956785 1.11684391
541 542 543 544 545 546 547 548 549
-31.95445271 -16.31043215 22.63303717 14.96174054 5.10437335 4.80174054 -20.19423889 13.99437335 -9.28821759
550 551 552 553 554 555 556 557 558
-14.49960165 -27.28781504 -25.36551773 -18.20050343 -15.16505325 -3.33419739 -4.23210300 32.63285573 26.67809435
559 560 561 562 563 564 565 566 567
27.83220567 16.29185367 5.36755770 25.94385367 15.47679522 18.02785573 -9.16412787 9.09498351 1.03233740
568 569 570 571 572 573 574 575 576
-5.41905035 -8.47531589 -11.57291738 3.21961346 -4.04766260 3.16831684 -3.41266260 -3.95291738 6.29632047
577 578 579 580 581 582 583 584 585
-1.53493205 -10.25547502 -4.43693213 1.15317086 1.81060622 -17.48534192 -40.77472487 24.25614323 22.09135637
586 587 588 589 590 591 592 593 594
25.65540822 -13.79780046 -31.92397473 -44.13943183 32.21210652 -21.45705031 -49.10822458 -61.17322458 6.72608861
595 596 597 598 599 600 601 602 603
-0.25528823 -9.01515628 8.32219762 -22.32878716 -22.58237301 -9.90898794 -1.07028457 -26.06626400 0.50234824
604 605 606 607 608 609 610 611 612
-18.70848185 -2.80617495 -7.98895449 -16.39020015 23.69920920 -11.38880027 12.53915333 -14.72688670 -9.70861024
613 614 615 616 617 618 619 620 621
-4.85899726 -12.74957265 -5.68657065 8.07464063 5.01374973 24.73499403 25.93390595 24.51698131 13.00841729
622 623 624 625 626 627 628 629 630
4.17807500 9.49499403 19.94653770 4.16708306 2.02013689 -20.38625027 -22.92625027 0.29374396 28.59812413
631 632 633 634 635 636 637 638 639
24.80779240 0.95217954 -3.60972995 -17.48625604 107.60452883 15.61590191 4.91112573 217.69884621 7.18527005
640 641 642 643 644 645 646 647 648
16.16452883 597.57779240 21.27217954 -8.35357082 31.15779240 9.09027005 21.69368519 15.41278933 14.44066911
649 650 651 652 653 654 655 656 657
8.71136368 31.92278933 33.80816911 21.41136368 23.38701852 -1.09721067 36.65136368 13.22701852 -3.63721067
658 659 660 661 662 663 664 665 666
-10.39967551 -18.98624253 -15.49909167 3.22787819 -21.62559181 23.70766662 25.25843743 19.52381721 8.39701178
667 668 669 670 671 672 673 674 675
46.29864524 27.51766662 16.36843743 16.30447734 14.55650196 13.85603017 5.19553025 26.76286518 14.12558565
676 677 678 679 680 681 682 683 684
14.84130768 30.44960296 14.95109285 3.53314902 -18.06890715 29.17960296 32.73109285 11.35551759 14.68232916
685 686 687 688 689 690 691 692 693
29.60293630 -28.21685098 -14.04448241 -29.31602672 11.95475814 39.20502012 32.02802172 26.46904386 28.05724234
694 695 696 697 698 699 700 701 702
9.80302172 17.06240886 22.11475814 17.91665850 8.56809148 48.67150075 32.54960039 24.87486236 3.97881553
703 704 705 706 707 708 709 710 711
51.21150075 33.81960039 25.50986236 14.52286396 17.88986236 20.73150075 41.43960039 -36.21670776 10.46450800
712 713 714 715 716 717 718 719 720
-3.32105766 -25.09549200 -28.72105766 1.49750927 -8.40105766 -5.62988166 11.95057870 45.06996551 59.92430994
721 722 723 724 725 726 727 728 729
60.85258107 36.36849967 15.52651606 36.66449760 36.48151606 31.29570675 35.84651606 23.67570675 21.42449760
730 731 732 733 734 735 736 737 738
-34.12756085 9.27516633 -27.68972578 -29.02581165 -12.95017973 -2.54547782 -23.59778828 -12.55366625 8.04715360
739 740 741 742 743 744 745 746 747
4.43952218 -13.15668774 -35.30972578 -54.19490256 49.94775660 35.37362147 19.18040995 -20.49945393 -15.46562838
748 749 750 751 752 753 754 755 756
-13.37083991 -20.11537045 -18.90789436 -12.87612270 -54.55808976 -51.05795364 8.99285579 -60.45820171 -70.25739229
757 758 759 760 761 762 763 764 765
-2.48696348 -11.47719267 -18.73581290 -14.38332412 -18.70610709 -16.56535836 -9.97800736 -10.46649802 -12.65902409
766 767 768 769 770 771 772 773 774
9.44440247 -15.19902409 -8.33559753 22.47296299 22.01447938 7.55767007 6.26940247 15.59379632 9.56847938
775 776 777 778 779 780 781 782 783
3.24506148 -12.03418837 -29.66515431 -56.73120728 2.43226586 -23.91134222 -9.53343822 -33.51440416 3.16656178
784 785 786 787 788 789 790 791 792
-25.89440416 -33.91045713 -15.90725874 32.06989819 11.95549190 -4.57322845 -15.21547051 17.03549190 -15.21547051
793 794 795 796 797 798 799 800 801
-12.34346851 -2.46861075 2.00158115 16.67315001 12.41874829 25.34567938 31.06671626 16.15805592 -2.23175219
802 803 804 805 806 807 808 809 810
-2.37684999 -0.36591838 31.69567938 3.45805592 -13.23841885 -36.66684999 32.48466892 35.65070563 36.90139286
811 812 813 814 815 816 817 818 819
36.16500510 26.97040623 26.83613807 40.11560644 -1.35346126 -8.75386382 -43.17524787 -23.36679459 1.19155603
820 821 822 823 824 825 826 827 828
20.56794795 9.54585195 6.73155268 4.21883304 15.99523876 14.17869810 9.50660210 3.94063616 2.90958319
829 830 831 832 833 834 835 836 837
4.13548038 4.09880944 8.23520136 -11.67689464 -27.32479864 3.15623783 -3.42605529 8.72610758 8.26560376
838 839 840 841 842 843 844 845 846
13.64616491 1.59329406 3.31650166 -5.58250773 41.96454311 16.51606449 3.00322419 -8.09361826 -1.01709036
847 848 849 850 851 852 853 854 855
26.72454311 6.03856449 6.10766863 16.76290964 -8.34962479 -6.29545689 -5.64543551 13.91971507 -4.79362479
856 857 858 859 860 861 862 863 864
-30.36810218 -4.18190514 4.81017751 29.35268507 0.89809486 24.15337207 28.08268507 18.27949758 14.84003874
865 866 867 868 869 870 871 872 873
4.38684418 27.44768507 2.19283091 13.45948101 0.89809486 -0.39996126 7.60417751 14.11268507 -2.15944885
874 875 876 877 878 879 880 881 882
29.56249503 19.10930047 18.39291914 10.14195387 7.01527063 -8.20225238 20.80263380 28.83514136 17.76195387
883 884 885 886 887 888 889 890 891
2.78193730 -11.92417164 13.18263380 14.91614709 14.41490220 -39.90285025 -39.90285025 27.07565234 -15.38367705
892 893 894 895 896 897 898 899 900
6.49217058 16.49507534 10.26200638 -14.86737019 3.79507534 -15.13799362 5.18200638 -3.19179047 43.11035135
901 902 903 904 905 906 907 908 909
4.64359310 10.47160950 4.65080018 13.14996174 21.25169648 0.30775699 12.16829740 -16.09173008 5.50369648
910 911 912 913 914 915 916 917 918
-13.66224301 27.32412063 25.82077338 -4.76503593 26.33169648 21.47442366 -11.64422662 13.01496407 18.38952672
919 920 921 922 923 924 925 926 927
-5.74047328 -9.62460619 -8.15814429 -24.03385581 -14.50121969 -24.30041027 -26.60400916 -29.56671572 -34.51135581
928 929 930 931 932 933 934 935 936
0.73878031 -10.33041027 -24.06400916 -22.86968914 -9.10371969 -8.42541027 5.81878031 -14.62623530 -32.04775424
937 938 939 940 941 942 943 944 945
-41.41557625 -44.43983253 -9.48332497 -9.80384579 -0.57689267 4.87194795 7.76618646 3.42395503 -30.09197130
946 947 948 949 950 951 952 953 954
-64.75983253 -47.83732497 12.21118646 11.04395503 -29.30191518 -15.35997130 -23.27316586 -49.10732497 -49.76052903
955 956 957 958 959 960 961 962 963
45.34717587 15.12756239 38.17838998 6.01106286 27.74584268 28.22722774 15.77831207 17.99661835 16.62523059
964 965 966 967 968 969 970 971 972
-9.84863644 -23.98590480 30.79384268 36.69389441 35.37259779 53.98843395 46.65557389 46.04773992 40.59202085
973 974 975 976 977 978 979 980 981
2.51840098 -25.50009872 -12.77371219 -7.71243255 -33.59467460 -17.38767260 -12.33805797 -14.32992445 15.06906775
982 983 984 985 986 987 988 989 990
3.15682570 22.53882769 -9.01414121 -19.16508580 -21.50896086 -30.07901455 -14.89792327 -31.07592127 22.90218382
991 992 993 994 995 996 997 998 999
4.07892813 -4.39532662 -2.49781618 12.65142813 -12.65032662 -35.29355433 15.28218382 4.38231406 -1.51916071
1000
-1.76999357
[ reached 'max' / getOption("max.print") -- omitted 5633 entries ]plot(lepgib.lm)
Post-hoc comparisons
lepgib.emm <- emmeans(lepgib.lm, ~ begin_date_year*age_group)
pairs(lepgib.emm, simple = "age_group")begin_date_year = 1992:
contrast estimate SE df t.ratio p.value
age_group1 - age_group2 -28.82 1.460 6610 -19.738 <.0001
age_group1 - age_group3 -55.21 1.400 6610 -39.395 <.0001
age_group1 - age_group4 -76.79 1.400 6610 -54.680 <.0001
age_group1 - age_group5 -93.73 1.430 6610 -65.406 <.0001
age_group1 - age_group6 -105.88 1.500 6610 -70.549 <.0001
age_group1 - age_group7 -116.67 1.640 6610 -71.008 <.0001
age_group1 - age_group8 -128.42 2.070 6610 -62.038 <.0001
age_group1 - age_group9 -137.31 2.710 6610 -50.579 <.0001
age_group1 - age_group10 -142.94 4.350 6610 -32.866 <.0001
age_group2 - age_group3 -26.38 1.000 6610 -26.374 <.0001
age_group2 - age_group4 -47.97 1.000 6610 -47.834 <.0001
age_group2 - age_group5 -64.91 1.040 6610 -62.355 <.0001
age_group2 - age_group6 -77.06 1.130 6610 -68.160 <.0001
age_group2 - age_group7 -87.84 1.310 6610 -66.855 <.0001
age_group2 - age_group8 -99.60 1.820 6610 -54.792 <.0001
age_group2 - age_group9 -108.49 2.530 6610 -42.909 <.0001
age_group2 - age_group10 -114.11 4.240 6610 -26.937 <.0001
age_group3 - age_group4 -21.58 0.905 6610 -23.858 <.0001
age_group3 - age_group5 -38.53 0.945 6610 -40.757 <.0001
age_group3 - age_group6 -50.67 1.040 6610 -48.656 <.0001
age_group3 - age_group7 -61.46 1.240 6610 -49.621 <.0001
age_group3 - age_group8 -73.21 1.760 6610 -41.543 <.0001
age_group3 - age_group9 -82.11 2.490 6610 -32.983 <.0001
age_group3 - age_group10 -87.73 4.210 6610 -20.817 <.0001
age_group4 - age_group5 -16.94 0.945 6610 -17.935 <.0001
age_group4 - age_group6 -29.09 1.040 6610 -27.970 <.0001
age_group4 - age_group7 -39.88 1.240 6610 -32.222 <.0001
age_group4 - age_group8 -51.63 1.760 6610 -29.321 <.0001
age_group4 - age_group9 -60.53 2.490 6610 -24.322 <.0001
age_group4 - age_group10 -66.15 4.210 6610 -15.697 <.0001
age_group5 - age_group6 -12.15 1.070 6610 -11.315 <.0001
age_group5 - age_group7 -22.93 1.270 6610 -18.115 <.0001
age_group5 - age_group8 -34.69 1.780 6610 -19.486 <.0001
age_group5 - age_group9 -43.58 2.500 6610 -17.416 <.0001
age_group5 - age_group10 -49.21 4.220 6610 -11.652 <.0001
age_group6 - age_group7 -10.79 1.340 6610 -8.067 <.0001
age_group6 - age_group8 -22.54 1.830 6610 -12.315 <.0001
age_group6 - age_group9 -31.44 2.540 6610 -12.384 <.0001
age_group6 - age_group10 -37.06 4.240 6610 -8.731 <.0001
age_group7 - age_group8 -11.75 1.950 6610 -6.028 <.0001
age_group7 - age_group9 -20.65 2.630 6610 -7.864 <.0001
age_group7 - age_group10 -26.27 4.300 6610 -6.114 <.0001
age_group8 - age_group9 -8.90 2.910 6610 -3.060 0.0680
age_group8 - age_group10 -14.52 4.480 6610 -3.244 0.0392
age_group9 - age_group10 -5.62 4.810 6610 -1.169 0.9769
P value adjustment: tukey method for comparing a family of 10 estimates test(pairs(lepgib.emm, by = "begin_date_year"), by = NULL, adjust = "mvt") contrast begin_date_year estimate SE df t.ratio p.value
age_group1 - age_group2 1992 -28.82 1.460 6610 -19.738 <.0001
age_group1 - age_group3 1992 -55.21 1.400 6610 -39.395 <.0001
age_group1 - age_group4 1992 -76.79 1.400 6610 -54.680 <.0001
age_group1 - age_group5 1992 -93.73 1.430 6610 -65.406 <.0001
age_group1 - age_group6 1992 -105.88 1.500 6610 -70.549 <.0001
age_group1 - age_group7 1992 -116.67 1.640 6610 -71.008 <.0001
age_group1 - age_group8 1992 -128.42 2.070 6610 -62.038 <.0001
age_group1 - age_group9 1992 -137.31 2.710 6610 -50.579 <.0001
age_group1 - age_group10 1992 -142.94 4.350 6610 -32.866 <.0001
age_group2 - age_group3 1992 -26.38 1.000 6610 -26.374 <.0001
age_group2 - age_group4 1992 -47.97 1.000 6610 -47.834 <.0001
age_group2 - age_group5 1992 -64.91 1.040 6610 -62.355 <.0001
age_group2 - age_group6 1992 -77.06 1.130 6610 -68.160 <.0001
age_group2 - age_group7 1992 -87.84 1.310 6610 -66.855 <.0001
age_group2 - age_group8 1992 -99.60 1.820 6610 -54.792 <.0001
age_group2 - age_group9 1992 -108.49 2.530 6610 -42.909 <.0001
age_group2 - age_group10 1992 -114.11 4.240 6610 -26.937 <.0001
age_group3 - age_group4 1992 -21.58 0.905 6610 -23.858 <.0001
age_group3 - age_group5 1992 -38.53 0.945 6610 -40.757 <.0001
age_group3 - age_group6 1992 -50.67 1.040 6610 -48.656 <.0001
age_group3 - age_group7 1992 -61.46 1.240 6610 -49.621 <.0001
age_group3 - age_group8 1992 -73.21 1.760 6610 -41.543 <.0001
age_group3 - age_group9 1992 -82.11 2.490 6610 -32.983 <.0001
age_group3 - age_group10 1992 -87.73 4.210 6610 -20.817 <.0001
age_group4 - age_group5 1992 -16.94 0.945 6610 -17.935 <.0001
age_group4 - age_group6 1992 -29.09 1.040 6610 -27.970 <.0001
age_group4 - age_group7 1992 -39.88 1.240 6610 -32.222 <.0001
age_group4 - age_group8 1992 -51.63 1.760 6610 -29.321 <.0001
age_group4 - age_group9 1992 -60.53 2.490 6610 -24.322 <.0001
age_group4 - age_group10 1992 -66.15 4.210 6610 -15.697 <.0001
age_group5 - age_group6 1992 -12.15 1.070 6610 -11.315 <.0001
age_group5 - age_group7 1992 -22.93 1.270 6610 -18.115 <.0001
age_group5 - age_group8 1992 -34.69 1.780 6610 -19.486 <.0001
age_group5 - age_group9 1992 -43.58 2.500 6610 -17.416 <.0001
age_group5 - age_group10 1992 -49.21 4.220 6610 -11.652 <.0001
age_group6 - age_group7 1992 -10.79 1.340 6610 -8.067 <.0001
age_group6 - age_group8 1992 -22.54 1.830 6610 -12.315 <.0001
age_group6 - age_group9 1992 -31.44 2.540 6610 -12.384 <.0001
age_group6 - age_group10 1992 -37.06 4.240 6610 -8.731 <.0001
age_group7 - age_group8 1992 -11.75 1.950 6610 -6.028 <.0001
age_group7 - age_group9 1992 -20.65 2.630 6610 -7.864 <.0001
age_group7 - age_group10 1992 -26.27 4.300 6610 -6.114 <.0001
age_group8 - age_group9 1992 -8.90 2.910 6610 -3.060 0.0546
age_group8 - age_group10 1992 -14.52 4.480 6610 -3.244 0.0311
age_group9 - age_group10 1992 -5.62 4.810 6610 -1.169 0.9704
P value adjustment: mvt method for 45 tests #export tables
# #interpret(eta_squared(lepgib.lm), rules = "cohen1992") %>%
# write.csv(file = "Outputs/Tables/lepgib_eta_squared.csv")
#slope contrasts
# Obtain slope estimates
lepgib.slopes <- emtrends(lepgib.lm, ~age_group, var = "begin_date_year")
# Compare estimated slopes to 0
lepgib.slope.contrasts <- test(lepgib.slopes) %>%
mutate(Species = "Pumpkinseed Sunfish") %>%
rename(Age = age_group)
lepgib.slope.contrasts %>%
write.csv(file = "Outputs/Tables/lepgib_emmeans.csv")Plot raw data
(lepgib.length.year.plot <- ggplot(data = lepgib %>%
mutate(Age = as.factor(age_group)),
aes(x = begin_date_year, y = length_mean_mm,
color = Age, fill = Age, group = Age))+
#geom_point()+
geom_smooth(method = "lm")+
stat_poly_eq(use_label("eq"))+
scale_color_viridis_d()+
scale_fill_viridis_d()+
labs(y = "Length (mm)", x = "Year")+
theme_bw()
)`geom_smooth()` using formula = 'y ~ x'
Marginal Effects
result <- predict_response(lepgib.lm, c("begin_date_year", "age_group"))
# plot(result)
# test_predictions(result) %>%
# write.csv(file = "Outputs/Tables/lepgib_pairwise_length_time_slopes.csv", row.names = F)
(lepgib.marginal.plot <- ggpredict(lepgib.lm, terms = c("begin_date_year", "age_group")) %>%
plot()+
#stat_poly_eq(use_label("eq"))+
stat_regline_equation()+
stat_poly_line()+
# annotate(geom = "text", label = "y = -220 + 0.22x", x = 1990, y = 220)+
# annotate(geom = "text", label = "y = -1.7 + 0.11x", x = 1990, y = 213)+
# annotate(geom = "text", label = "y = -64 + 0.13x", x = 1990, y = 205)+
# annotate(geom = "text", label = "y = 300 + 0.25x", x = 1990, y = 195)+
# annotate(geom = "text", label = "y = -170 + 0.18x", x = 1990, y = 185)+
# annotate(geom = "text", label = "y = -140 + 0.15x", x = 1990, y = 173)+
# annotate(geom = "text", label = "y = 42 + 0.06x", x = 1990, y = 160)+
# annotate(geom = "text", label = "y = 320 - 0.09x", x = 1990, y = 140)+
# annotate(geom = "text", label = "y = 850 - 0.37x", x = 1990, y = 115)+
# annotate(geom = "text", label = "y = 1.4E3 - 0.67x", x = 1990, y = 90)+
scale_color_viridis_d()+
scale_fill_viridis_d()+
labs(title = NULL,
y = "Mean Length-at-Age (mm)",
x = "Year")+
guides(fill = guide_legend(title = "Age", reverse = T), color = guide_legend(title = "Age", reverse = T))+
theme_bw()
)Scale for colour is already present.
Adding another scale for colour, which will replace the existing scale.ggsave(filename = "~/GitHub/EAGER_growth/Outputs/Figures/Linear Models/lepgib_marginal_effects_plot.tiff",
lepgib.marginal.plot,
dpi = 300, width = 200, height = 150, units = "mm")
Rainbow Trout
Model
#filter data for only Black Crappie, Pomooxis nigromaculatus
oncmyk <- all.grow.merge %>% filter(species == "rainbow_trout") %>%
filter(age_group %in% c(1, 2, 3, 4), #match age groups in BRTs
!is.na(age_group), !is.na(begin_date_year), !is.na(length_mean_mm),
!is.na(log_max_depth), !is.na(logarea))
#linear model
oncmyk.lm <- lm(length_mean_mm ~ begin_date_year*age_group + log_max_depth + logarea + doy, data = oncmyk)
summary(oncmyk.lm)
Call:
lm(formula = length_mean_mm ~ begin_date_year * age_group + log_max_depth +
logarea + doy, data = oncmyk)
Residuals:
Min 1Q Median 3Q Max
-231.167 -43.176 -1.245 41.354 239.066
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -6.795e+02 9.133e+02 -0.744 0.4573
begin_date_year 4.125e-01 4.574e-01 0.902 0.3677
age_group2 -1.707e+03 1.180e+03 -1.446 0.1489
age_group3 -2.139e+03 1.248e+03 -1.714 0.0873 .
age_group4 -6.222e+03 1.560e+03 -3.989 7.87e-05 ***
log_max_depth 1.033e+01 7.562e+00 1.367 0.1725
logarea 4.497e+00 2.267e+00 1.983 0.0480 *
doy 3.275e-01 5.364e-02 6.105 2.42e-09 ***
begin_date_year:age_group2 9.014e-01 5.947e-01 1.516 0.1304
begin_date_year:age_group3 1.161e+00 6.289e-01 1.847 0.0655 .
begin_date_year:age_group4 3.264e+00 7.854e-01 4.155 3.97e-05 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 71.3 on 402 degrees of freedom
(7 observations deleted due to missingness)
Multiple R-squared: 0.6034, Adjusted R-squared: 0.5935
F-statistic: 61.15 on 10 and 402 DF, p-value: < 2.2e-16#calculate and interpret effect sizes
eta_squared(oncmyk.lm) begin_date_year age_group log_max_depth logarea
0.005846741 0.531921899 0.005800480 0.004134132
doy begin_date_year:age_group
0.038417048 0.017230464 #interpret(eta_squared(oncmyk.lm), rules = "cohen1992")
#calculate AIC score
AIC(oncmyk.lm)[1] 4709.405#examine model fit
testDispersion(oncmyk.lm)
DHARMa nonparametric dispersion test via sd of residuals fitted vs. simulated
data: simulationOutput
dispersion = 0.97527, p-value = 0.744
alternative hypothesis: two.sided
simulation.output <- simulateResiduals(fittedModel = oncmyk.lm)
residuals(oncmyk.lm) 1 2 3 4 5 6 7 8 9
-22.9336552 28.5744336 26.0788029 10.7944336 -19.3699013 29.3525142 23.0181404 -50.9806279 90.4646867
10 11 12 13 14 15 16 17 18
79.3440396 80.8269820 103.6869820 187.5069820 -122.1744299 -30.6902655 -15.4512180 -158.0079082 -42.5435989
19 20 21 22 23 24 25 26 27
-58.3913894 -71.0913894 -115.3914376 43.6376217 18.5377523 8.1904454 -27.2179721 -138.8967454 100.8448875
28 29 30 31 32 33 34 35 36
-27.6754285 66.0845912 79.1788786 -16.4570952 90.6848875 104.8651681 -20.6869736 23.9116225 40.2087024
37 38 39 40 41 42 43 44 45
-6.0114180 62.8582892 30.4137246 47.1341379 -99.9313871 11.7022021 6.2947509 -78.5371391 3.3667096
46 47 48 49 50 51 52 53 54
-190.0245836 -25.6315754 -43.1763424 -92.3880592 -18.2473926 -96.5664621 -25.9862827 -130.2165858 -18.2735844
55 56 57 58 59 60 61 62 63
-17.4446679 -63.8627385 -120.1673253 -22.3130012 -52.3420242 -27.3930012 31.2186945 14.7086945 16.9212433
64 65 66 67 68 69 70 71 72
-35.3590333 -76.1002576 -198.9176445 -7.1780093 -86.2593847 -63.6793223 95.3712752 -107.4023402 118.2312752
73 74 75 76 77 78 79 80 81
-84.5423402 90.2912752 92.6102287 146.0454081 -36.0782283 68.7346778 10.2828572 -117.6193149 25.9136541
82 83 84 85 86 87 88 89 90
17.6945075 -33.9099271 14.0584693 114.3221699 78.2492548 169.4934019 14.5040728 14.5040728 -0.1573809
91 92 93 94 95 96 97 98 99
36.2160688 -8.7987780 -5.9012879 48.7663626 38.5309296 -33.2505699 -77.3260994 -8.4853159 -10.8818417
100 101 102 103 104 105 106 107 108
-2.7866901 5.6589524 14.3582099 59.9718760 40.2599767 56.4948945 44.5243617 -11.0300433 -18.3477662
109 110 111 112 113 114 115 116 117
-73.6103930 38.9530743 71.7314179 39.8183472 114.7873417 24.0546967 18.6071950 -92.4100950 52.5456530
118 119 120 121 122 123 124 125 126
-34.3379007 74.0538181 -37.7085593 -68.0909523 73.7229136 -32.6913323 -25.0633607 -38.4313436 25.1265933
127 128 129 130 131 132 133 134 135
-45.6628935 -121.0349219 -58.9312453 0.6281748 -49.6445341 33.4343440 55.5939851 -73.8894263 -42.6040770
136 137 138 139 140 141 142 143 144
-24.4911717 150.1696818 125.3046474 -80.7668498 -98.4889129 42.1471742 60.6065763 -49.1940588 68.7695727
145 146 147 148 149 150 151 152 153
25.0251920 61.0933412 124.8194088 -59.7228827 -32.3838812 4.5592461 -34.5569943 30.7195882 -7.8025709
154 155 156 157 158 159 160 161 162
-41.3366328 -52.3104461 -82.8930136 -69.8654742 -97.2937041 12.0126402 -8.4136937 128.9027441 -12.1222221
163 164 165 166 167 168 169 170 171
-48.4942505 108.9715345 6.0863900 -24.6689754 -14.4471333 39.3530466 37.4438654 -74.2090461 57.8565329
172 173 174 175 176 177 178 179 180
55.9473517 -91.6621507 -102.9062975 -37.2197489 -12.2045696 -51.3965288 -70.3868320 -27.8860477 -67.0701945
181 182 183 184 185 186 187 188 189
-36.6329313 110.8792821 3.8888153 55.4121822 85.8659175 3.3234804 51.9793961 92.5931314 -29.2700593
190 191 192 193 194 195 196 197 198
-18.2138246 30.1642705 -55.4844409 -59.4273016 -90.3534947 -44.9740564 -75.3209537 -74.1172530 -49.4678099
199 200 201 202 203 204 205 206 207
3.4102395 38.4254760 24.7977877 -10.7490751 131.4646602 59.4027349 16.4327957 -23.8497142 -71.2587824
208 209 210 211 212 213 214 215 216
-39.0360090 124.1413642 -14.6938389 80.1634174 85.2190942 -48.7222757 -18.1285852 103.6008792 129.0912563
217 218 219 220 221 222 223 224 225
-45.1697049 67.7274345 97.7454838 5.7941031 0.9090301 -5.4561690 62.4138294 63.4764411 -26.1162648
226 227 228 229 230 231 232 233 234
-42.3784418 24.2104421 140.2265209 -26.4145666 22.6028373 -166.9893921 -122.9321358 49.7635410 86.7192784
235 236 237 238 239 240 241 242 243
6.2053727 34.1214762 150.7709406 50.6734903 -35.9820085 -100.2454260 -1.2449801 69.8507500 5.9232632
244 245 246 247 248 249 250 251 252
28.6814973 20.0361813 42.0454107 23.2400947 33.4238729 17.3596092 25.5891037 38.2891037 -43.0400850
253 254 255 256 257 258 259 260 261
39.2348328 -53.9742067 -42.3781439 61.2210082 -115.9464566 -97.7799779 -148.6245924 -150.5709551 39.7560331
262 263 264 265 266 267 268 269 270
25.3826524 40.6540426 166.6020888 -141.9658668 -64.9118917 -6.7474745 4.4437902 68.2716763 -15.3753390
271 272 273 274 275 276 277 278 279
37.2526325 -33.6592998 17.1338570 7.0317940 -21.8327169 -36.5489069 -93.8317675 -119.6937182 -22.5466245
280 281 282 283 284 285 286 287 288
-34.1094852 0.5653176 9.3231167 -38.4703353 19.9712103 43.7156639 64.5022119 -50.9407657 -43.2905762
289 290 291 292 293 294 295 296 297
0.3559717 -30.1367927 -19.2659046 -80.2203470 -13.3560833 -13.3560833 -80.2203470 -27.4263115 30.5588128
298 299 300 301 302 303 304 305 306
11.3759522 119.6450241 170.9152246 -76.8549219 1.1093346 -60.6549464 108.7786127 110.7165568 92.6740144
307 308 312 313 314 315 316 317 318
105.2524931 239.0658786 134.3260264 151.0348141 151.5814313 118.8600906 -38.4727029 -3.2723074 -124.4939731
319 320 321 322 323 324 325 326 327
66.6308962 -105.7313882 -129.6624960 25.0216489 -62.0351313 99.7262422 48.6295156 -35.2527729 -66.7580890
328 329 331 332 333 334 335 336 337
-68.7902166 109.4075204 22.6400885 -28.8590430 -46.5010368 -46.6633128 49.0377716 -158.7953521 -231.1667929
338 339 340 341 342 343 344 345 346
10.8886347 54.2066408 70.3107024 150.1476079 30.9602730 -47.3272474 55.9848891 -7.3771047 -16.4059859
347 348 349 350 351 352 353 354 355
66.3261873 57.1611051 6.5993657 -56.6019749 -23.4347685 -13.9289956 -15.0719732 -41.3715513 66.7033834
356 357 358 360 361 362 363 364 365
-17.5649816 21.8272337 6.2110479 -92.6498921 -105.5457450 -95.7985360 -9.2543193 19.6805985 -62.0674725
366 367 368 369 370 371 372 373 374
-100.9179571 -138.5248188 182.9840734 -21.2335646 -69.6058022 0.1493309 -12.7330387 23.6712866 49.6991765
375 376 377 378 379 380 381 382 383
-6.9190152 -19.4452426 8.5164619 -20.3444295 -25.1221524 32.4001215 112.7524332 29.7841992 -12.1434544
384 385 386 389 390 391 392 393 394
-92.4897185 -86.7876443 -12.0969669 -22.1244107 -68.0654566 -97.5860961 -65.9101259 41.5851473 22.9469814
395 396 397 398 399 400 401 402 403
52.6817508 -3.3332835 41.5036221 -44.5652903 30.5596540 41.3543247 34.5038728 33.5089512 -0.6968986
404 405 406 407 408 409 410 411 412
42.1861469 6.1116031 -34.2194383 27.4008252 28.6767434 -70.3448940 -62.0911304 13.0161116 141.1143271
413 414 415 416 417 418 419 420
-95.8610844 61.9352792 4.8198428 -3.3685455 -24.1733228 -6.9190152 29.1491992 -21.1967163 residuals(oncmyk.lm, quantileFunction = qnorm) 1 2 3 4 5 6 7 8 9
-22.9336552 28.5744336 26.0788029 10.7944336 -19.3699013 29.3525142 23.0181404 -50.9806279 90.4646867
10 11 12 13 14 15 16 17 18
79.3440396 80.8269820 103.6869820 187.5069820 -122.1744299 -30.6902655 -15.4512180 -158.0079082 -42.5435989
19 20 21 22 23 24 25 26 27
-58.3913894 -71.0913894 -115.3914376 43.6376217 18.5377523 8.1904454 -27.2179721 -138.8967454 100.8448875
28 29 30 31 32 33 34 35 36
-27.6754285 66.0845912 79.1788786 -16.4570952 90.6848875 104.8651681 -20.6869736 23.9116225 40.2087024
37 38 39 40 41 42 43 44 45
-6.0114180 62.8582892 30.4137246 47.1341379 -99.9313871 11.7022021 6.2947509 -78.5371391 3.3667096
46 47 48 49 50 51 52 53 54
-190.0245836 -25.6315754 -43.1763424 -92.3880592 -18.2473926 -96.5664621 -25.9862827 -130.2165858 -18.2735844
55 56 57 58 59 60 61 62 63
-17.4446679 -63.8627385 -120.1673253 -22.3130012 -52.3420242 -27.3930012 31.2186945 14.7086945 16.9212433
64 65 66 67 68 69 70 71 72
-35.3590333 -76.1002576 -198.9176445 -7.1780093 -86.2593847 -63.6793223 95.3712752 -107.4023402 118.2312752
73 74 75 76 77 78 79 80 81
-84.5423402 90.2912752 92.6102287 146.0454081 -36.0782283 68.7346778 10.2828572 -117.6193149 25.9136541
82 83 84 85 86 87 88 89 90
17.6945075 -33.9099271 14.0584693 114.3221699 78.2492548 169.4934019 14.5040728 14.5040728 -0.1573809
91 92 93 94 95 96 97 98 99
36.2160688 -8.7987780 -5.9012879 48.7663626 38.5309296 -33.2505699 -77.3260994 -8.4853159 -10.8818417
100 101 102 103 104 105 106 107 108
-2.7866901 5.6589524 14.3582099 59.9718760 40.2599767 56.4948945 44.5243617 -11.0300433 -18.3477662
109 110 111 112 113 114 115 116 117
-73.6103930 38.9530743 71.7314179 39.8183472 114.7873417 24.0546967 18.6071950 -92.4100950 52.5456530
118 119 120 121 122 123 124 125 126
-34.3379007 74.0538181 -37.7085593 -68.0909523 73.7229136 -32.6913323 -25.0633607 -38.4313436 25.1265933
127 128 129 130 131 132 133 134 135
-45.6628935 -121.0349219 -58.9312453 0.6281748 -49.6445341 33.4343440 55.5939851 -73.8894263 -42.6040770
136 137 138 139 140 141 142 143 144
-24.4911717 150.1696818 125.3046474 -80.7668498 -98.4889129 42.1471742 60.6065763 -49.1940588 68.7695727
145 146 147 148 149 150 151 152 153
25.0251920 61.0933412 124.8194088 -59.7228827 -32.3838812 4.5592461 -34.5569943 30.7195882 -7.8025709
154 155 156 157 158 159 160 161 162
-41.3366328 -52.3104461 -82.8930136 -69.8654742 -97.2937041 12.0126402 -8.4136937 128.9027441 -12.1222221
163 164 165 166 167 168 169 170 171
-48.4942505 108.9715345 6.0863900 -24.6689754 -14.4471333 39.3530466 37.4438654 -74.2090461 57.8565329
172 173 174 175 176 177 178 179 180
55.9473517 -91.6621507 -102.9062975 -37.2197489 -12.2045696 -51.3965288 -70.3868320 -27.8860477 -67.0701945
181 182 183 184 185 186 187 188 189
-36.6329313 110.8792821 3.8888153 55.4121822 85.8659175 3.3234804 51.9793961 92.5931314 -29.2700593
190 191 192 193 194 195 196 197 198
-18.2138246 30.1642705 -55.4844409 -59.4273016 -90.3534947 -44.9740564 -75.3209537 -74.1172530 -49.4678099
199 200 201 202 203 204 205 206 207
3.4102395 38.4254760 24.7977877 -10.7490751 131.4646602 59.4027349 16.4327957 -23.8497142 -71.2587824
208 209 210 211 212 213 214 215 216
-39.0360090 124.1413642 -14.6938389 80.1634174 85.2190942 -48.7222757 -18.1285852 103.6008792 129.0912563
217 218 219 220 221 222 223 224 225
-45.1697049 67.7274345 97.7454838 5.7941031 0.9090301 -5.4561690 62.4138294 63.4764411 -26.1162648
226 227 228 229 230 231 232 233 234
-42.3784418 24.2104421 140.2265209 -26.4145666 22.6028373 -166.9893921 -122.9321358 49.7635410 86.7192784
235 236 237 238 239 240 241 242 243
6.2053727 34.1214762 150.7709406 50.6734903 -35.9820085 -100.2454260 -1.2449801 69.8507500 5.9232632
244 245 246 247 248 249 250 251 252
28.6814973 20.0361813 42.0454107 23.2400947 33.4238729 17.3596092 25.5891037 38.2891037 -43.0400850
253 254 255 256 257 258 259 260 261
39.2348328 -53.9742067 -42.3781439 61.2210082 -115.9464566 -97.7799779 -148.6245924 -150.5709551 39.7560331
262 263 264 265 266 267 268 269 270
25.3826524 40.6540426 166.6020888 -141.9658668 -64.9118917 -6.7474745 4.4437902 68.2716763 -15.3753390
271 272 273 274 275 276 277 278 279
37.2526325 -33.6592998 17.1338570 7.0317940 -21.8327169 -36.5489069 -93.8317675 -119.6937182 -22.5466245
280 281 282 283 284 285 286 287 288
-34.1094852 0.5653176 9.3231167 -38.4703353 19.9712103 43.7156639 64.5022119 -50.9407657 -43.2905762
289 290 291 292 293 294 295 296 297
0.3559717 -30.1367927 -19.2659046 -80.2203470 -13.3560833 -13.3560833 -80.2203470 -27.4263115 30.5588128
298 299 300 301 302 303 304 305 306
11.3759522 119.6450241 170.9152246 -76.8549219 1.1093346 -60.6549464 108.7786127 110.7165568 92.6740144
307 308 312 313 314 315 316 317 318
105.2524931 239.0658786 134.3260264 151.0348141 151.5814313 118.8600906 -38.4727029 -3.2723074 -124.4939731
319 320 321 322 323 324 325 326 327
66.6308962 -105.7313882 -129.6624960 25.0216489 -62.0351313 99.7262422 48.6295156 -35.2527729 -66.7580890
328 329 331 332 333 334 335 336 337
-68.7902166 109.4075204 22.6400885 -28.8590430 -46.5010368 -46.6633128 49.0377716 -158.7953521 -231.1667929
338 339 340 341 342 343 344 345 346
10.8886347 54.2066408 70.3107024 150.1476079 30.9602730 -47.3272474 55.9848891 -7.3771047 -16.4059859
347 348 349 350 351 352 353 354 355
66.3261873 57.1611051 6.5993657 -56.6019749 -23.4347685 -13.9289956 -15.0719732 -41.3715513 66.7033834
356 357 358 360 361 362 363 364 365
-17.5649816 21.8272337 6.2110479 -92.6498921 -105.5457450 -95.7985360 -9.2543193 19.6805985 -62.0674725
366 367 368 369 370 371 372 373 374
-100.9179571 -138.5248188 182.9840734 -21.2335646 -69.6058022 0.1493309 -12.7330387 23.6712866 49.6991765
375 376 377 378 379 380 381 382 383
-6.9190152 -19.4452426 8.5164619 -20.3444295 -25.1221524 32.4001215 112.7524332 29.7841992 -12.1434544
384 385 386 389 390 391 392 393 394
-92.4897185 -86.7876443 -12.0969669 -22.1244107 -68.0654566 -97.5860961 -65.9101259 41.5851473 22.9469814
395 396 397 398 399 400 401 402 403
52.6817508 -3.3332835 41.5036221 -44.5652903 30.5596540 41.3543247 34.5038728 33.5089512 -0.6968986
404 405 406 407 408 409 410 411 412
42.1861469 6.1116031 -34.2194383 27.4008252 28.6767434 -70.3448940 -62.0911304 13.0161116 141.1143271
413 414 415 416 417 418 419 420
-95.8610844 61.9352792 4.8198428 -3.3685455 -24.1733228 -6.9190152 29.1491992 -21.1967163 plot(oncmyk.lm)
Post-hoc comparisons
oncmyk.emm <- emmeans(oncmyk.lm, ~ begin_date_year*age_group)
pairs(oncmyk.emm, simple = "age_group")begin_date_year = 1983:
contrast estimate SE df t.ratio p.value
age_group1 - age_group2 -81.3 9.00 402 -9.026 <.0001
age_group1 - age_group3 -164.5 9.90 402 -16.618 <.0001
age_group1 - age_group4 -251.2 12.60 402 -19.935 <.0001
age_group2 - age_group3 -83.3 9.56 402 -8.712 <.0001
age_group2 - age_group4 -169.9 12.30 402 -13.867 <.0001
age_group3 - age_group4 -86.6 12.70 402 -6.842 <.0001
P value adjustment: tukey method for comparing a family of 4 estimates test(pairs(oncmyk.emm, by = "begin_date_year"), by = NULL, adjust = "mvt") contrast begin_date_year estimate SE df t.ratio p.value
age_group1 - age_group2 1983 -81.3 9.00 402 -9.026 <.0001
age_group1 - age_group3 1983 -164.5 9.90 402 -16.618 <.0001
age_group1 - age_group4 1983 -251.2 12.60 402 -19.935 <.0001
age_group2 - age_group3 1983 -83.3 9.56 402 -8.712 <.0001
age_group2 - age_group4 1983 -169.9 12.30 402 -13.867 <.0001
age_group3 - age_group4 1983 -86.6 12.70 402 -6.842 <.0001
P value adjustment: mvt method for 6 tests #export tables
# #interpret(eta_squared(oncmyk.lm), rules = "cohen1992") %>%
# write.csv(file = "Outputs/Tables/oncmyk_eta_squared.csv")
#slope contrasts
# Obtain slope estimates
oncmyk.slopes <- emtrends(oncmyk.lm, ~age_group, var = "begin_date_year")
# Compare estimated slopes to 0
oncmyk.slope.contrasts <- test(oncmyk.slopes) %>%
mutate(Species = "Rainbow Trout") %>%
rename(Age = age_group)
oncmyk.slope.contrasts %>%
write.csv(file = "Outputs/Tables/oncmyk_emmeans.csv")Plot raw data
(oncmyk.length.year.plot <- ggplot(data = oncmyk %>%
mutate(Age = as.factor(age_group)),
aes(x = begin_date_year, y = length_mean_mm,
color = Age, fill = Age, group = Age))+
#geom_point()+
geom_smooth(method = "lm")+
stat_poly_eq(use_label("eq"))+
scale_color_viridis_d()+
scale_fill_viridis_d()+
labs(y = "Length (mm)", x = "Year")+
theme_bw()
)`geom_smooth()` using formula = 'y ~ x'
Marginal Effects
result <- predict_response(oncmyk.lm, c("begin_date_year", "age_group"))
# plot(result)
# test_predictions(result) %>%
# write.csv(file = "Outputs/Tables/oncmyk_pairwise_length_time_slopes.csv", row.names = F)
(oncmyk.marginal.plot <- ggpredict(oncmyk.lm, terms = c("begin_date_year", "age_group")) %>%
plot()+
#stat_poly_eq(use_label("eq"))+
stat_regline_equation()+
stat_poly_line()+
# annotate(geom = "text", label = "y = -5.8E3 + 3.2x", x = 2000, y = 600)+
# annotate(geom = "text", label = "y = -2.2E3 + 1.3x", x = 2000, y = 475)+
# annotate(geom = "text", label = "y = -1.3E3 + 0.82x", x = 2000, y = 380)+
# annotate(geom = "text", label = "y = 920 - 0.33x", x = 2000, y = 285)+
scale_color_viridis_d()+
scale_fill_viridis_d()+
labs(title = NULL,
y = "Mean Length-at-Age (mm)",
x = "Year")+
guides(fill = guide_legend(title = "Age", reverse = T), color = guide_legend(title = "Age", reverse = T))+
theme_bw()
)Scale for colour is already present.
Adding another scale for colour, which will replace the existing scale.ggsave(filename = "~/GitHub/EAGER_growth/Outputs/Figures/Linear Models/oncmyk_marginal_effects_plot.tiff",
oncmyk.marginal.plot,
dpi = 300, width = 200, height = 150, units = "mm")
Rock Bass
Model
#filter data for only Black Crappie, Pomooxis nigromaculatus
ambrup <- all.grow.merge %>% filter(species == "rock_bass") %>%
filter(age_group %in% c(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11), #match age groups in BRTs
!is.na(age_group), !is.na(begin_date_year), !is.na(length_mean_mm),
!is.na(log_max_depth), !is.na(logarea))
#linear model
ambrup.lm <- lm(length_mean_mm ~ begin_date_year*age_group + log_max_depth + logarea + doy, data = ambrup)
summary(ambrup.lm)
Call:
lm(formula = length_mean_mm ~ begin_date_year * age_group + log_max_depth +
logarea + doy, data = ambrup)
Residuals:
Min 1Q Median 3Q Max
-87.429 -16.193 -0.899 16.416 83.713
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 1.432e+03 2.868e+02 4.994 6.14e-07 ***
begin_date_year -6.909e-01 1.437e-01 -4.806 1.59e-06 ***
age_group2 -6.671e+02 3.168e+02 -2.106 0.035293 *
age_group3 -1.010e+03 3.070e+02 -3.291 0.001008 **
age_group4 -1.168e+03 3.049e+02 -3.831 0.000129 ***
age_group5 -1.149e+03 3.073e+02 -3.739 0.000187 ***
age_group6 -1.359e+03 3.132e+02 -4.339 1.46e-05 ***
age_group7 -1.082e+03 3.194e+02 -3.389 0.000708 ***
age_group8 -1.202e+03 3.308e+02 -3.633 0.000283 ***
age_group9 -1.117e+03 3.588e+02 -3.112 0.001867 **
age_group10 -7.675e+02 4.021e+02 -1.909 0.056337 .
age_group11 -6.557e+02 5.266e+02 -1.245 0.213109
log_max_depth -7.402e-01 5.200e-01 -1.423 0.154684
logarea 1.996e+00 2.364e-01 8.443 < 2e-16 ***
doy 6.279e-02 8.023e-03 7.826 6.24e-15 ***
begin_date_year:age_group2 3.495e-01 1.589e-01 2.200 0.027828 *
begin_date_year:age_group3 5.369e-01 1.539e-01 3.488 0.000491 ***
begin_date_year:age_group4 6.300e-01 1.529e-01 4.120 3.85e-05 ***
begin_date_year:age_group5 6.324e-01 1.541e-01 4.104 4.14e-05 ***
begin_date_year:age_group6 7.482e-01 1.570e-01 4.765 1.95e-06 ***
begin_date_year:age_group7 6.184e-01 1.601e-01 3.862 0.000114 ***
begin_date_year:age_group8 6.864e-01 1.658e-01 4.139 3.55e-05 ***
begin_date_year:age_group9 6.500e-01 1.798e-01 3.616 0.000302 ***
begin_date_year:age_group10 4.805e-01 2.013e-01 2.386 0.017061 *
begin_date_year:age_group11 4.291e-01 2.633e-01 1.630 0.103268
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 24.07 on 4509 degrees of freedom
(73 observations deleted due to missingness)
Multiple R-squared: 0.8155, Adjusted R-squared: 0.8145
F-statistic: 830.3 on 24 and 4509 DF, p-value: < 2.2e-16#calculate and interpret effect sizes
eta_squared(ambrup.lm) begin_date_year age_group log_max_depth logarea
2.017039e-04 8.084486e-01 1.414964e-05 2.301937e-03
doy begin_date_year:age_group
2.905206e-03 1.601735e-03 #interpret(eta_squared(ambrup.lm), rules = "cohen1992")
#calculate AIC score
AIC(ambrup.lm)[1] 41737.49#examine model fit
testDispersion(ambrup.lm)
DHARMa nonparametric dispersion test via sd of residuals fitted vs. simulated
data: simulationOutput
dispersion = 0.9953, p-value = 0.88
alternative hypothesis: two.sided
simulation.output <- simulateResiduals(fittedModel = ambrup.lm)
residuals(ambrup.lm) 1 2 3 4 5 6 7 8 9
-10.83527069 -11.99326310 -29.37620149 -16.20089322 -9.74453699 6.12529728 0.32256107 -9.14193736 -32.31326310
10 11 12 13 14 15 16 17 18
-30.54036816 17.97863061 6.67256107 12.29409834 12.29409834 7.21409834 -2.59311630 -28.25472616 -24.44472616
19 20 21 22 23 24 25 26 27
-27.29605189 -9.34786118 -29.65030375 13.37573195 -23.33130177 -33.54124496 -23.08084579 -32.91909057 -23.02070042
28 29 30 31 32 33 34 35 36
-15.15186275 -20.72262368 -30.03347153 -37.09882638 -42.38359441 -33.66073745 -33.40581635 -40.10297003 -17.87390105
37 38 39 40 41 42 43 44 45
-7.07330518 -16.69083023 -46.97695308 -52.47556700 -17.50919838 -9.31582689 -11.58458782 9.64950644 1.43291218
46 47 48 49 50 51 52 53 54
20.37956432 -4.70567942 7.45491763 14.84729841 -0.89978049 10.49617311 18.65729841 30.85021951 -28.09458782
55 56 57 58 59 60 61 62 63
13.07021951 10.58145621 23.14072138 43.32077602 61.78025449 64.39276409 47.26960625 -6.24533259 26.55577873
64 65 66 67 68 69 70 71 72
33.09798723 61.29413236 57.21797529 62.44681745 28.35565660 47.14630954 40.48801023 74.42718650 52.22402866
73 74 75 76 77 78 79 80 81
43.36428099 39.38735616 -9.68351877 -7.93367436 -18.37785084 -25.40600566 -21.79241878 -20.14651319 -2.82826992
82 83 84 85 86 87 88 89 90
-11.19111425 -6.99666841 -22.67445761 -11.07080082 1.34428684 -14.18840759 17.35534799 -12.43124642 -56.11029193
91 92 93 94 95 96 97 98 99
-7.15540164 9.10034799 -1.25524642 -7.38966982 -5.31029193 -2.07540164 10.24926111 -13.75965201 7.88875358
100 101 102 103 104 105 106 107 108
-7.38966982 -18.01029193 -7.15540164 1.05159241 5.84741593 -10.07073889 -1.90631868 9.15875358 2.77033018
109 110 111 112 113 114 115 116 117
11.14741956 29.76783276 36.69599169 57.55695626 30.63781615 13.11663076 40.05554397 63.30320290 47.33416746
118 119 120 121 122 123 124 125 126
58.92427406 47.25931841 -35.19708177 -24.96457217 -16.68773002 -11.57747768 16.42862140 -27.56736097 -21.83051881
127 128 129 130 131 132 133 134 135
-7.83026647 -14.34719131 3.50669239 -17.41805527 -9.32998010 2.56920853 24.18887205 -23.66988327 -38.53393945
136 137 138 139 140 141 142 143 144
-48.12999878 -43.90389669 26.63396430 16.68811645 0.23887271 4.12632690 6.07585053 7.25361795 -13.51527477
145 146 147 148 149 150 151 152 153
6.31396430 -8.71188355 6.58887271 5.04396430 -1.09188355 -7.38112729 -3.49367310 0.99585053 21.55396430
154 155 156 157 158 159 160 161 162
6.52811645 -1.66612729 2.94017830 -12.17168188 -0.33430121 0.84525782 18.46780021 1.22392579 -20.82696456
163 164 165 166 167 168 169 170 171
-22.96668188 4.07798509 33.70780021 24.44678293 17.16890029 16.41677335 0.69170451 3.86196996 28.20491218
172 173 174 175 176 177 178 179 180
50.62269681 5.77738509 7.79580569 -4.38829549 -18.57738336 18.65027957 32.13122190 44.99309950 35.72169684
181 182 183 184 185 186 187 188 189
-24.08071670 23.73027957 24.17255523 35.72169684 32.31724066 8.49027957 8.08588856 51.46969684 38.03224066
190 191 192 193 194 195 196 197 198
37.69254512 48.42250147 55.82602714 42.29011355 10.98788198 18.48571437 6.36048771 28.67554512 46.64450147
199 200 201 202 203 204 205 206 207
47.84120573 50.54511355 23.16394221 18.60788198 33.24754512 42.92511355 31.18571437 15.23005682 6.76922441
208 209 210 211 212 213 214 215 216
11.83542784 6.93829948 4.15825367 3.14444397 -7.09930159 6.01554473 14.67481841 -5.72258432 -12.31263013
217 218 219 220 221 222 223 224 225
3.92439350 -5.47268540 0.86882759 13.57675687 -8.28985192 18.44090434 -6.74324313 -2.78900407 -8.28985192
226 227 228 229 230 231 232 233 234
19.71090434 -19.58164147 -30.33211783 23.77080327 7.67586237 2.35423767 -4.34164147 -6.70919673 21.19675687
235 236 237 238 239 240 241 242 243
0.38599593 9.06681475 21.61590434 -32.96997196 -1.73798389 -13.78114173 -58.20088939 -41.62479032 -20.86049349
244 245 246 247 248 249 250 251 252
1.45885827 -26.61997196 -36.10049349 -32.21798389 -34.10114173 -36.18755606 -35.93114756 -30.82979032 -16.97798389
253 254 255 256 257 258 259 260 261
-59.50114173 -68.36088939 -22.41199926 -1.09488129 -7.16285968 -13.45680475 -12.34110574 -52.68501412 -44.57743884
262 263 264 265 266 267 268 269 270
-20.93160814 -17.75533259 8.68411871 -7.16285968 -14.72680475 -35.20110574 -1.47588129 -35.20110574 -18.39160814
271 272 273 274 275 276 277 278 279
-19.02533259 0.42911871 10.61714032 -30.45187360 -57.76501412 -43.01002708 -45.12494010 -35.94497925 -33.28463237
280 281 282 283 284 285 286 287 288
-13.98617789 2.28910478 -9.71043762 -6.86122824 -17.87141313 -5.04299353 3.98503331 -12.37868402 -33.90322642
289 290 291 292 293 294 295 296 297
-10.31420193 2.51421768 -6.91508882 -16.25147281 -18.18172950 -28.32280584 -26.88699072 -15.43032021 -46.71457994
298 299 300 301 302 303 304 305 306
-2.53951971 28.36307629 -0.95443838 -3.23224039 -5.29050147 -13.00536931 -17.96541999 -22.67710113 -17.90789033
307 308 309 310 311 312 313 314 315
-20.90923354 -20.71164959 -27.50335150 25.12371644 -4.76443838 -21.91535150 9.98206541 1.64895776 -4.32056314
316 317 318 319 320 321 322 323 324
12.39896196 6.61448647 2.89702461 4.99891621 -6.02346521 -10.72498338 1.59783113 -8.49556563 -7.10939198
325 326 327 328 329 330 331 332 333
0.19928228 -17.76668118 -16.85599161 1.38450932 2.35510645 7.24101513 -8.87132194 -12.87404628 -19.04100201
334 335 336 337 338 339 340 341 342
-18.89323800 -13.61936084 -17.46022405 -4.59992911 -16.57797476 -15.26742479 44.90174289 46.28590847 51.45743009
343 344 345 346 347 348 349 350 351
50.87848501 28.18168402 22.13118162 21.49745718 59.93168402 32.29118162 22.13245718 58.66168402 27.21118162
352 353 354 355 356 357 358 359 360
15.62747760 13.90847386 -4.27591714 -1.34120620 -18.40899775 -6.49817896 -7.93552614 -3.64091714 -29.98010886
361 362 363 364 365 366 367 368 369
21.86043496 -19.92013330 -24.16585573 -20.80485947 -12.53091714 5.57989114 -5.93472966 -7.99974769 -5.38723809
370 371 372 373 374 375 376 377 378
-11.08039593 -16.13014359 2.75293158 -4.63404452 -13.53389075 -37.74845464 -30.17939241 -34.64729843 -32.52887813
379 380 381 382 383 384 385 386 387
-42.54002838 -29.15523707 -39.00948419 4.54688834 -46.44724343 -36.04789549 -61.80342056 -33.99503940 -35.14469253
388 389 390 391 392 393 394 395 396
-2.11196742 -35.33403223 -43.73068429 -25.16782820 -27.58748133 14.58131074 3.88636404 -0.15290911 -21.71488750
397 398 399 400 401 402 403 404 405
6.70450076 7.39686644 -9.44736041 6.22511250 0.71263959 21.46511250 -17.84883257 -21.81313356 -17.58667907
406 407 408 409 410 411 412 413 414
-2.76752693 -25.99010400 12.61068353 -35.44716734 -22.33918272 -25.83997334 -7.64015822 -21.98117549 -21.63905814
415 416 417 418 419 420 421 422 423
-7.36916477 -17.60894982 -15.18492299 -7.16464031 -3.28918272 -13.77497334 -15.26015822 -37.22117549 -16.55443012
424 425 426 427 428 429 430 431 432
-12.78109620 -3.40230038 9.57029517 -10.49610749 2.60943633 -51.87113193 32.07637239 -18.48413641 -13.19938818
433 434 435 436 437 438 439 440 441
-6.48053459 -12.26409077 -14.87515010 -12.35235225 -12.00976229 10.77670553 17.73242723 -14.57130025 -11.86815035
442 443 444 445 446 447 448 449 450
5.39765244 5.01918184 25.22216457 25.11057793 14.99937577 -7.07687784 31.44658697 -23.14245802 11.43713170
451 452 453 454 455 456 457 458 459
1.34258930 26.20161379 22.18976667 4.20058549 -1.24199159 -4.55120406 -6.83501376 21.34310001 3.35391882
460 461 462 463 464 465 466 467 468
6.37800841 6.45546261 26.42310001 3.77725215 25.42800841 3.06879594 -21.31209266 39.47546261 4.99622645
469 470 471 472 473 474 475 476 477
10.07622645 -13.98053008 25.12786006 -1.34331888 7.28507127 12.05374553 -9.77113025 -12.49709371 -20.65907687
478 479 480 481 482 483 484 485 486
0.95894475 -4.06500033 16.19894475 -18.18930132 10.04679031 12.36092313 -14.28105525 -2.79500033 12.29069868
487 488 489 490 491 492 493 494 495
-3.28006917 0.08261018 -4.26786604 40.87175166 30.70280687 38.82570181 23.32017675 18.72753297 9.02803391
496 497 498 499 500 501 502 503 504
14.23196437 12.38746594 60.33614020 22.96086631 7.75803391 17.93877829 -8.80837378 -3.22487752 -8.85358670
505 506 507 508 509 510 511 512 513
-13.27910304 -13.77596024 -3.55212718 -3.09439474 -6.15152800 -1.68468584 -0.12466077 3.36447500 9.52416557
514 515 516 517 518 519 520 521 522
6.01329553 8.18860400 21.61389580 1.74010429 -6.11233555 -23.89249859 -15.62593383 21.19250141 24.82102617
523 524 525 526 527 528 529 530 531
-10.32490501 15.19822228 23.39643424 11.69374469 -1.39519269 -22.62249859 25.35822228 31.16707803 -3.65079531
532 533 534 535 536 537 538 539 540
1.06309938 7.76230978 -6.97949662 16.65322313 29.25362121 51.16806052 44.59138661 40.40292876 36.68485799
541 542 543 544 545 546 547 548 549
10.52670820 3.96364391 13.83785432 25.34271458 19.13043433 -5.09916759 36.71193839 44.52859782 37.80013997
550 551 552 553 554 555 556 557 558
4.11391941 6.01752179 3.61506552 58.93492579 37.17137695 29.22580902 40.27735117 -6.86233495 9.70363935
559 560 561 562 563 564 565 566 567
9.70224173 13.39578547 36.02020129 10.26403215 -22.43223644 51.12886954 39.89552897 45.86707112 18.53085056
568 569 570 571 572 573 574 575 576
33.98111960 0.88699667 43.50685694 42.37457669 56.24497477 30.49208075 54.22607350 7.88472843 22.41420787
577 578 579 580 581 582 583 584 585
28.83906814 52.47178789 40.94218597 23.48662528 11.82995137 1.14180741 24.63540978 2.54795352 58.50281379
586 587 588 589 590 591 592 593 594
11.01553354 30.40038672 28.34903270 48.55071106 55.48499970 23.20933136 54.32818172 38.51628865 30.46317552
595 596 597 598 599 600 601 602 603
45.37539293 25.75349985 51.34683164 -8.93424489 -33.58626680 -29.25424489 -10.12938463 16.04333513 9.34321137
604 605 606 607 608 609 610 611 612
16.46575511 22.04394871 6.26656035 3.22910408 9.65396435 -6.46361027 -2.05849663 0.21597830 7.47666787
613 614 615 616 617 618 619 620 621
5.89747183 2.76943260 5.28727533 19.60930320 -18.22472490 -16.45182997 38.11776593 25.97013247 34.84930320
622 623 624 625 626 627 628 629 630
-1.82673250 -32.61805824 -72.53333213 22.14930320 -23.15512265 -9.75805824 -8.40849663 -42.96402170 -0.56138750
631 632 633 634 635 636 637 638 639
26.71224415 24.55073601 23.83591104 -4.59937175 -2.34359124 3.72597072 4.82123111 -1.92203125 -4.48015766
640 641 642 643 644 645 646 647 648
20.05666759 3.39810786 7.08393964 18.74610902 -0.05311931 -31.49590190 -14.92279497 -15.99946318 9.27918859
649 650 651 652 653 654 655 656 657
5.72562731 -2.61470332 15.99624859 -2.25193624 2.87421160 0.64562731 20.24529668 7.74124859 10.49421160
658 659 660 661 662 663 664 665 666
3.22303706 -10.07802882 -8.13795427 -7.16770448 20.24529668 6.21473043 -35.25371679 17.07272193 -6.57174318
667 668 669 670 671 672 673 674 675
-6.95025770 5.55649856 0.55395275 -7.65652362 30.99031278 -2.33840985 -15.24759104 3.01649856 12.57124380
676 677 678 679 680 681 682 683 684
27.58051749 -4.87840985 -27.52425770 31.62124380 -9.89715571 10.21891280 2.98135410 1.95733610 -7.92040122
685 686 687 688 689 690 691 692 693
-2.21045740 -6.47251673 -11.69671888 -3.47796851 -11.54435250 -19.52222824 -19.21301886 -20.78891803 -31.86422102
694 695 696 697 698 699 700 701 702
-33.66411167 -17.09805675 -14.41626612 -31.70869083 3.96163080 -15.16733864 -25.34848347 -10.22459939 -21.59149246
703 704 705 706 707 708 709 710 711
4.63683933 -14.49849130 -19.76353939 -4.82239088 -8.37457637 -11.20075091 -5.52964941 -1.93534342 3.67759233
712 713 714 715 716 717 718 719 720
13.49171319 19.56873682 2.44582459 13.44400424 14.08307322 -9.85651879 -21.79205137 19.27626119 30.19940100
721 722 723 724 725 726 727 728 729
28.26914431 32.20692512 23.73674024 8.43486527 17.23394514 44.92917524 18.29360798 36.96271733 13.09153194
730 731 732 733 734 735 736 737 738
32.72794514 25.17810408 58.73906864 1.66153194 26.37794514 7.50989371 5.22870831 -5.61487848 -7.99639736
739 740 741 742 743 744 745 746 747
33.17991369 23.33841713 1.43452024 -2.14941182 3.08912390 -1.25339423 9.73930106 19.30860264 -3.35700939
748 749 750 751 752 753 754 755 756
-3.07758287 -0.50487746 7.63660577 54.62660577 15.66596773 23.75360264 -7.22941182 -14.69087610 3.82660577
757 758 759 760 761 762 763 764 765
14.81930106 18.67360264 41.11841713 -7.07087610 19.06660577 4.43375816 -1.80468943 -11.14947202 -7.97974971
766 767 768 769 770 771 772 773 774
8.01182384 3.30006464 -27.16541202 10.67806982 -2.08523570 -8.26077833 -0.69814522 20.91217943 24.43956769
775 776 777 778 779 780 781 782 783
35.71526670 22.68449884 23.31135832 27.31717819 20.20014139 2.83806427 -4.28718168 -11.14443980 0.22508564
784 785 786 787 788 789 790 791 792
-10.07427184 -6.60931098 -3.73729744 18.15845543 -9.77101811 -31.20009866 -10.43250918 7.91975171 7.64419197
793 794 795 796 797 798 799 800 801
19.97307931 15.58385980 -0.27661686 -0.35170645 21.07277472 24.03736507 30.45878580 36.40053496 23.76834322
802 803 804 805 806 807 808 809 810
0.27576629 -0.92281298 47.83053496 21.65343019 -20.00185139 -23.11478698 -21.13022537 -53.78075044 -27.04672754
811 812 813 814 815 816 817 818 819
-38.86289327 -34.92896281 -30.56659627 13.87257446 -20.26346124 -15.49478698 -42.08522537 -19.85006087 -9.52896281
820 821 822 823 824 825 826 827 828
-10.24659627 -1.36742554 -38.35478698 -2.98075044 -38.86289327 16.41257446 -10.10346124 -25.65478698 -54.78522537
829 830 831 832 833 834 835 836 837
-53.78075044 -8.38289327 -14.18562947 -12.78659627 -11.52742554 -0.18383032 3.70458288 1.74274182 11.17370638
838 839 840 841 842 843 844 845 846
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937 938 939 940 941 942 943 944 945
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946 947 948 949 950 951 952 953 954
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955 956 957 958 959 960 961 962 963
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973 974 975 976 977 978 979 980 981
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982 983 984 985 986 987 988 989 990
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991 992 993 994 995 996 997 998 999
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1000
-54.06191406
[ reached 'max' / getOption("max.print") -- omitted 3534 entries ]residuals(ambrup.lm, quantileFunction = qnorm) 1 2 3 4 5 6 7 8 9
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10 11 12 13 14 15 16 17 18
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19 20 21 22 23 24 25 26 27
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28 29 30 31 32 33 34 35 36
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37 38 39 40 41 42 43 44 45
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46 47 48 49 50 51 52 53 54
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55 56 57 58 59 60 61 62 63
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64 65 66 67 68 69 70 71 72
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73 74 75 76 77 78 79 80 81
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172 173 174 175 176 177 178 179 180
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181 182 183 184 185 186 187 188 189
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190 191 192 193 194 195 196 197 198
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199 200 201 202 203 204 205 206 207
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208 209 210 211 212 213 214 215 216
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217 218 219 220 221 222 223 224 225
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226 227 228 229 230 231 232 233 234
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235 236 237 238 239 240 241 242 243
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253 254 255 256 257 258 259 260 261
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721 722 723 724 725 726 727 728 729
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874 875 876 877 878 879 880 881 882
-45.42461189 -46.56154566 -41.42275073 -29.56998370 1.50668503 -10.39427789 -16.45755031 -12.43049290 51.00446345
883 884 885 886 887 888 889 890 891
-28.95183229 -17.79201087 -42.12292447 5.81112945 13.56867319 20.62853345 28.38625320 5.52441669 -0.53747293
892 893 894 895 896 897 898 899 900
10.07470088 22.03533985 10.25686679 19.49625320 9.13775726 8.06441669 20.41612945 -1.67132681 23.80353345
901 902 903 904 905 906 907 908 909
24.15291987 -14.21366653 17.81999700 10.04188808 5.82612616 -5.53640932 8.00463617 2.49116301 8.60644568
910 911 912 913 914 915 916 917 918
1.51594174 -5.72972067 -1.07657223 -28.92629344 -46.20809984 -36.75704675 -38.90275978 -47.32720936 -33.16054994
919 920 921 922 923 924 925 926 927
-45.36143317 -59.82871342 -24.46720936 -33.48628154 -11.40903366 -24.22514958 -28.81870932 -39.63204420 -36.96570816
928 929 930 931 932 933 934 935 936
10.83172064 8.78286223 31.64461587 -4.89542327 15.12159027 15.34695197 18.87505397 2.15064001 13.86461587
937 938 939 940 941 942 943 944 945
7.80457673 6.86659027 -6.00171899 0.05118529 1.02571712 16.42039840 16.48820666 3.78996030 -1.00007884
946 947 948 949 950 951 952 953 954
2.00872302 1.94590264 4.81438969 20.86745725 16.47381084 10.37275466 22.22249318 0.67672991 7.44164937
955 956 957 958 959 960 961 962 963
25.85697797 38.10692830 11.31860666 22.68793845 22.40303639 9.92922640 12.27153916 10.83014971 -12.43255529
964 965 966 967 968 969 970 971 972
-14.66439643 -13.20148769 -8.48105497 -17.09304296 -16.07100755 -17.50972949 -6.80492134 16.39253591 19.66827483
973 974 975 976 977 978 979 980 981
24.18068477 21.28589599 17.11245295 22.19009232 34.24732053 11.83890449 0.39728567 -18.46213065 -31.50725489
982 983 984 985 986 987 988 989 990
-25.38603979 23.33432458 16.85334656 42.69599574 37.12406530 33.52376517 18.67036099 25.72884118 -15.68990830
991 992 993 994 995 996 997 998 999
1.27167565 7.17009170 -18.65030730 -40.37417640 -43.76070857 -46.40512349 -49.03239304 -47.58801515 -10.06312486
1000
-54.06191406
[ reached 'max' / getOption("max.print") -- omitted 3534 entries ]plot(ambrup.lm)
Post-hoc comparisons
ambrup.emm <- emmeans(ambrup.lm, ~ begin_date_year*age_group)
pairs(ambrup.emm, simple = "age_group")begin_date_year = 1994:
contrast estimate SE df t.ratio p.value
age_group1 - age_group2 -30.08 2.43 4509 -12.396 <.0001
age_group1 - age_group3 -60.68 2.34 4509 -25.898 <.0001
age_group1 - age_group4 -88.23 2.33 4509 -37.897 <.0001
age_group1 - age_group5 -112.27 2.35 4509 -47.847 <.0001
age_group1 - age_group6 -133.25 2.38 4509 -56.030 <.0001
age_group1 - age_group7 -151.10 2.43 4509 -62.278 <.0001
age_group1 - age_group8 -167.06 2.52 4509 -66.418 <.0001
age_group1 - age_group9 -179.91 2.70 4509 -66.709 <.0001
age_group1 - age_group10 -190.79 2.98 4509 -63.929 <.0001
age_group1 - age_group11 -200.07 3.80 4509 -52.712 <.0001
age_group2 - age_group3 -30.61 1.47 4509 -20.831 <.0001
age_group2 - age_group4 -58.16 1.45 4509 -40.238 <.0001
age_group2 - age_group5 -82.19 1.47 4509 -55.784 <.0001
age_group2 - age_group6 -103.18 1.52 4509 -67.797 <.0001
age_group2 - age_group7 -121.02 1.59 4509 -75.943 <.0001
age_group2 - age_group8 -136.98 1.72 4509 -79.461 <.0001
age_group2 - age_group9 -149.83 1.98 4509 -75.777 <.0001
age_group2 - age_group10 -160.71 2.35 4509 -68.264 <.0001
age_group2 - age_group11 -170.00 3.32 4509 -51.197 <.0001
age_group3 - age_group4 -27.55 1.29 4509 -21.351 <.0001
age_group3 - age_group5 -51.59 1.32 4509 -39.058 <.0001
age_group3 - age_group6 -72.57 1.37 4509 -52.870 <.0001
age_group3 - age_group7 -90.42 1.45 4509 -62.372 <.0001
age_group3 - age_group8 -106.38 1.59 4509 -66.883 <.0001
age_group3 - age_group9 -119.23 1.86 4509 -64.069 <.0001
age_group3 - age_group10 -130.11 2.26 4509 -57.639 <.0001
age_group3 - age_group11 -139.39 3.25 4509 -42.868 <.0001
age_group4 - age_group5 -24.03 1.29 4509 -18.604 <.0001
age_group4 - age_group6 -45.02 1.34 4509 -33.485 <.0001
age_group4 - age_group7 -62.86 1.42 4509 -44.190 <.0001
age_group4 - age_group8 -78.82 1.57 4509 -50.344 <.0001
age_group4 - age_group9 -91.67 1.84 4509 -49.832 <.0001
age_group4 - age_group10 -102.55 2.24 4509 -45.788 <.0001
age_group4 - age_group11 -111.84 3.24 4509 -34.522 <.0001
age_group5 - age_group6 -20.99 1.37 4509 -15.299 <.0001
age_group5 - age_group7 -38.83 1.45 4509 -26.823 <.0001
age_group5 - age_group8 -54.79 1.59 4509 -34.516 <.0001
age_group5 - age_group9 -67.64 1.86 4509 -36.422 <.0001
age_group5 - age_group10 -78.52 2.25 4509 -34.832 <.0001
age_group5 - age_group11 -87.81 3.25 4509 -27.028 <.0001
age_group6 - age_group7 -17.84 1.49 4509 -11.957 <.0001
age_group6 - age_group8 -33.81 1.63 4509 -20.774 <.0001
age_group6 - age_group9 -46.65 1.89 4509 -24.679 <.0001
age_group6 - age_group10 -57.53 2.28 4509 -25.215 <.0001
age_group6 - age_group11 -66.82 3.27 4509 -20.453 <.0001
age_group7 - age_group8 -15.96 1.69 4509 -9.454 <.0001
age_group7 - age_group9 -28.81 1.94 4509 -14.835 <.0001
age_group7 - age_group10 -39.69 2.32 4509 -17.073 <.0001
age_group7 - age_group11 -48.98 3.30 4509 -14.859 <.0001
age_group8 - age_group9 -12.85 2.04 4509 -6.288 <.0001
age_group8 - age_group10 -23.73 2.41 4509 -9.846 <.0001
age_group8 - age_group11 -33.02 3.35 4509 -9.841 <.0001
age_group9 - age_group10 -10.88 2.59 4509 -4.198 0.0014
age_group9 - age_group11 -20.17 3.49 4509 -5.786 <.0001
age_group10 - age_group11 -9.29 3.71 4509 -2.501 0.3038
P value adjustment: tukey method for comparing a family of 11 estimates test(pairs(ambrup.emm, by = "begin_date_year"), by = NULL, adjust = "mvt") contrast begin_date_year estimate SE df t.ratio p.value
age_group1 - age_group2 1994 -30.08 2.43 4509 -12.396 <.0001
age_group1 - age_group3 1994 -60.68 2.34 4509 -25.898 <.0001
age_group1 - age_group4 1994 -88.23 2.33 4509 -37.897 <.0001
age_group1 - age_group5 1994 -112.27 2.35 4509 -47.847 <.0001
age_group1 - age_group6 1994 -133.25 2.38 4509 -56.030 <.0001
age_group1 - age_group7 1994 -151.10 2.43 4509 -62.278 <.0001
age_group1 - age_group8 1994 -167.06 2.52 4509 -66.418 <.0001
age_group1 - age_group9 1994 -179.91 2.70 4509 -66.709 <.0001
age_group1 - age_group10 1994 -190.79 2.98 4509 -63.929 <.0001
age_group1 - age_group11 1994 -200.07 3.80 4509 -52.712 <.0001
age_group2 - age_group3 1994 -30.61 1.47 4509 -20.831 <.0001
age_group2 - age_group4 1994 -58.16 1.45 4509 -40.238 <.0001
age_group2 - age_group5 1994 -82.19 1.47 4509 -55.784 <.0001
age_group2 - age_group6 1994 -103.18 1.52 4509 -67.797 <.0001
age_group2 - age_group7 1994 -121.02 1.59 4509 -75.943 <.0001
age_group2 - age_group8 1994 -136.98 1.72 4509 -79.461 <.0001
age_group2 - age_group9 1994 -149.83 1.98 4509 -75.777 <.0001
age_group2 - age_group10 1994 -160.71 2.35 4509 -68.264 <.0001
age_group2 - age_group11 1994 -170.00 3.32 4509 -51.197 <.0001
age_group3 - age_group4 1994 -27.55 1.29 4509 -21.351 <.0001
age_group3 - age_group5 1994 -51.59 1.32 4509 -39.058 <.0001
age_group3 - age_group6 1994 -72.57 1.37 4509 -52.870 <.0001
age_group3 - age_group7 1994 -90.42 1.45 4509 -62.372 <.0001
age_group3 - age_group8 1994 -106.38 1.59 4509 -66.883 <.0001
age_group3 - age_group9 1994 -119.23 1.86 4509 -64.069 <.0001
age_group3 - age_group10 1994 -130.11 2.26 4509 -57.639 <.0001
age_group3 - age_group11 1994 -139.39 3.25 4509 -42.868 <.0001
age_group4 - age_group5 1994 -24.03 1.29 4509 -18.604 <.0001
age_group4 - age_group6 1994 -45.02 1.34 4509 -33.485 <.0001
age_group4 - age_group7 1994 -62.86 1.42 4509 -44.190 <.0001
age_group4 - age_group8 1994 -78.82 1.57 4509 -50.344 <.0001
age_group4 - age_group9 1994 -91.67 1.84 4509 -49.832 <.0001
age_group4 - age_group10 1994 -102.55 2.24 4509 -45.788 <.0001
age_group4 - age_group11 1994 -111.84 3.24 4509 -34.522 <.0001
age_group5 - age_group6 1994 -20.99 1.37 4509 -15.299 <.0001
age_group5 - age_group7 1994 -38.83 1.45 4509 -26.823 <.0001
age_group5 - age_group8 1994 -54.79 1.59 4509 -34.516 <.0001
age_group5 - age_group9 1994 -67.64 1.86 4509 -36.422 <.0001
age_group5 - age_group10 1994 -78.52 2.25 4509 -34.832 <.0001
age_group5 - age_group11 1994 -87.81 3.25 4509 -27.028 <.0001
age_group6 - age_group7 1994 -17.84 1.49 4509 -11.957 <.0001
age_group6 - age_group8 1994 -33.81 1.63 4509 -20.774 <.0001
age_group6 - age_group9 1994 -46.65 1.89 4509 -24.679 <.0001
age_group6 - age_group10 1994 -57.53 2.28 4509 -25.215 <.0001
age_group6 - age_group11 1994 -66.82 3.27 4509 -20.453 <.0001
age_group7 - age_group8 1994 -15.96 1.69 4509 -9.454 <.0001
age_group7 - age_group9 1994 -28.81 1.94 4509 -14.835 <.0001
age_group7 - age_group10 1994 -39.69 2.32 4509 -17.073 <.0001
age_group7 - age_group11 1994 -48.98 3.30 4509 -14.859 <.0001
age_group8 - age_group9 1994 -12.85 2.04 4509 -6.288 <.0001
age_group8 - age_group10 1994 -23.73 2.41 4509 -9.846 <.0001
age_group8 - age_group11 1994 -33.02 3.35 4509 -9.841 <.0001
age_group9 - age_group10 1994 -10.88 2.59 4509 -4.198 0.0015
age_group9 - age_group11 1994 -20.17 3.49 4509 -5.786 <.0001
age_group10 - age_group11 1994 -9.29 3.71 4509 -2.501 0.2777
P value adjustment: mvt method for 55 tests #export tables
# #interpret(eta_squared(ambrup.lm), rules = "cohen1992") %>%
# write.csv(file = "Outputs/Tables/ambrup_eta_squared.csv")
#slope contrasts
# Obtain slope estimates
ambrup.slopes <- emtrends(ambrup.lm, ~age_group, var = "begin_date_year")
# Compare estimated slopes to 0
ambrup.slope.contrasts <- test(ambrup.slopes) %>%
mutate(Species = "Rock Bass") %>%
rename(Age = age_group)
ambrup.slope.contrasts %>%
write.csv(file = "Outputs/Tables/ambrup_emmeans.csv")Plot raw data
(ambrup.length.year.plot <- ggplot(data = ambrup %>%
mutate(Age = as.factor(age_group)),
aes(x = begin_date_year, y = length_mean_mm,
color = Age, fill = Age, group = Age))+
#geom_point()+
geom_smooth(method = "lm")+
stat_poly_eq(use_label("eq"))+
scale_color_viridis_d()+
scale_fill_viridis_d()+
labs(y = "Length (mm)", x = "Year")+
theme_bw()
)`geom_smooth()` using formula = 'y ~ x'
Marginal Effects
result <- predict_response(ambrup.lm, c("begin_date_year", "age_group"))
# plot(result)
# test_predictions(result) %>%
# write.csv(file = "Outputs/Tables/ambrup_pairwise_length_time_slopes.csv", row.names = F)
(ambrup.marginal.plot <- ggpredict(ambrup.lm, terms = c("begin_date_year", "age_group")) %>%
plot()+
#stat_poly_eq(use_label("eq"))+
stat_regline_equation()+
stat_poly_line()+
# annotate(geom = "text", label = "y = 940 - 0.33x", x = 1990, y = 285)+
# annotate(geom = "text", label = "y = 760 - 0.25x", x = 1990, y = 270)+
# annotate(geom = "text", label = "y = 430 - 0.09x", x = 1990, y = 258)+
# annotate(geom = "text", label = "y = 390 - 0.08x", x = 1990, y = 245)+
# annotate(geom = "text", label = "y = 500 - 0.14x", x = 1990, y = 230)+
# annotate(geom = "text", label = "y = 210 - 0.004x", x = 1990, y = 215)+
# annotate(geom = "text", label = "y = 16 + 0.09x", x = 1990, y = 198)+
# annotate(geom = "text", label = "y = 430 - 0.13x", x = 1990, y = 170)+
# annotate(geom = "text", label = "y = 580 - 0.22x", x = 1990, y = 145)+
# annotate(geom = "text", label = "y = 940 - 0.42x", x = 1990, y = 125)+
# annotate(geom = "text", label = "y = 1.8E3 - 0.85x", x = 1990, y = 90)+
scale_color_viridis_d()+
scale_fill_viridis_d()+
labs(title = NULL,
y = "Mean Length-at-Age (mm)",
x = "Year")+
guides(fill = guide_legend(title = "Age", reverse = T), color = guide_legend(title = "Age", reverse = T))+
theme_bw()
)Scale for colour is already present.
Adding another scale for colour, which will replace the existing scale.ggsave(filename = "~/GitHub/EAGER_growth/Outputs/Figures/Linear Models/ambrup_marginal_effects_plot.tiff",
ambrup.marginal.plot,
dpi = 300, width = 200, height = 150, units = "mm")
Smallmouth Bass
Model
#filter data for only Black Crappie, Pomooxis nigromaculatus
micdol <- all.grow.merge %>% filter(species == "smallmouth_bass") %>%
filter(age_group %in% c(0:10), #match age groups in BRTs
!is.na(age_group), !is.na(begin_date_year), !is.na(length_mean_mm),
!is.na(log_max_depth), !is.na(logarea))
#linear model
micdol.lm <- lm(length_mean_mm ~ begin_date_year*age_group + log_max_depth + logarea + doy, data = micdol)
summary(micdol.lm)
Call:
lm(formula = length_mean_mm ~ begin_date_year * age_group + log_max_depth +
logarea + doy, data = micdol)
Residuals:
Min 1Q Median 3Q Max
-131.802 -23.219 0.962 23.339 305.938
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 1.795e+02 6.186e+02 0.290 0.7717
begin_date_year -7.542e-02 3.123e-01 -0.242 0.8092
age_group1 1.328e+03 6.653e+02 1.996 0.0460 *
age_group2 5.222e+02 6.476e+02 0.806 0.4201
age_group3 4.624e+01 6.450e+02 0.072 0.9428
age_group4 -2.427e+02 6.507e+02 -0.373 0.7092
age_group5 -2.826e+02 6.601e+02 -0.428 0.6686
age_group6 -2.417e+02 6.684e+02 -0.362 0.7177
age_group7 3.444e+02 6.835e+02 0.504 0.6144
age_group8 -4.965e+01 7.048e+02 -0.070 0.9438
age_group9 8.656e+01 7.563e+02 0.114 0.9089
age_group10 8.955e+02 8.401e+02 1.066 0.2865
log_max_depth -1.832e+00 8.940e-01 -2.049 0.0405 *
logarea 5.144e+00 4.245e-01 12.118 <2e-16 ***
doy 1.684e-01 1.445e-02 11.652 <2e-16 ***
begin_date_year:age_group1 -6.395e-01 3.357e-01 -1.905 0.0569 .
begin_date_year:age_group2 -1.998e-01 3.268e-01 -0.611 0.5411
begin_date_year:age_group3 7.034e-02 3.255e-01 0.216 0.8289
begin_date_year:age_group4 2.436e-01 3.283e-01 0.742 0.4582
begin_date_year:age_group5 2.849e-01 3.330e-01 0.856 0.3923
begin_date_year:age_group6 2.809e-01 3.371e-01 0.833 0.4048
begin_date_year:age_group7 -2.749e-04 3.446e-01 -0.001 0.9994
begin_date_year:age_group8 2.077e-01 3.552e-01 0.585 0.5588
begin_date_year:age_group9 1.494e-01 3.808e-01 0.392 0.6948
begin_date_year:age_group10 -2.475e-01 4.224e-01 -0.586 0.5579
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 37.88 on 3601 degrees of freedom
(38 observations deleted due to missingness)
Multiple R-squared: 0.8824, Adjusted R-squared: 0.8816
F-statistic: 1126 on 24 and 3601 DF, p-value: < 2.2e-16#calculate and interpret effect sizes
eta_squared(micdol.lm) begin_date_year age_group log_max_depth logarea
8.839238e-03 8.628581e-01 5.133296e-07 4.226395e-03
doy begin_date_year:age_group
4.847208e-03 1.652199e-03 #interpret(eta_squared(micdol.lm), rules = "cohen1992")
#calculate AIC score
AIC(micdol.lm)[1] 36673.54#examine model fit
testDispersion(micdol.lm)
DHARMa nonparametric dispersion test via sd of residuals fitted vs. simulated
data: simulationOutput
dispersion = 0.9932, p-value = 0.712
alternative hypothesis: two.sided
simulation.output <- simulateResiduals(fittedModel = micdol.lm)
residuals(micdol.lm) 1 2 3 4 5 6 7 8
15.75956500 -27.51880699 -11.51680699 -19.29651469 -32.19925295 -8.75462771 -10.22543483 26.50915349
9 10 11 12 13 14 15 16
-58.04470911 -25.16186591 -14.65281367 -26.08826465 -7.78084651 -12.46186591 16.54096304 18.01813409
17 18 19 20 21 22 23 24
-19.94470911 20.38971943 12.90687623 -10.32071541 -22.47680003 2.16720675 -17.11285236 -22.32257081
25 26 27 28 29 30 31 32
-19.60960035 -11.48976978 -29.05288091 34.63518509 -36.31027599 -66.58989614 -61.43761553 -20.47866617
33 34 35 36 37 38 39 40
-16.47928019 -21.55920102 -39.50738701 3.80058559 -24.81674832 33.52855844 -13.63957897 -25.48904281
41 42 43 44 45 46 47 48
-22.35144156 1.39883372 13.77210004 -3.50788601 -48.30221387 -40.13144156 16.27597658 -6.18504281
49 50 51 52 53 54 55 56
-26.30241108 9.25758892 -52.41667807 -29.80030494 37.43281121 -11.77667807 -27.33718879 -22.18030494
57 58 59 60 61 62 63 64
47.65290901 11.83113262 -10.82718879 -38.22937087 3.63137472 -80.68944939 20.89408181 9.31771335
65 66 67 68 69 70 71 72
12.95929869 20.55725249 -48.16911597 -14.34544159 26.50485277 16.53734681 -36.64533333 48.30326197
73 74 75 76 77 78 79 80
-25.01780766 -41.77710179 10.99873191 23.07266032 -43.65271444 -9.07689372 20.14273191 -39.15733968
81 82 83 84 85 86 87 88
7.14728556 -48.45742994 -92.57710179 30.66343624 -10.89810370 -42.20960558 -27.75656376 10.46550020
89 90 91 92 93 94 95 96
62.43813770 -26.47493303 61.75658489 91.06364677 40.82713188 -3.90452352 -15.43111066 21.54719823
97 98 99 100 101 102 103 104
-48.98130762 -44.06691399 -46.37312263 -36.46550200 -33.74130762 -7.23691399 -31.13312263 -61.84691399
105 106 107 108 109 110 111 112
-0.49983539 -7.57699145 -44.05794185 -49.58874746 -38.45135384 -22.63889581 -45.37439874 -2.60338458
113 114 115 116 117 118 119 120
-27.82590167 2.47661542 -29.25939207 -27.59439874 -12.74939207 1.15575108 -20.36939207 -2.60338458
121 122 123 124 125 126 127 128
8.77575108 -36.48439874 -6.42773207 -40.07991136 -18.55948702 -16.27716984 -24.82838458 -19.35923500
129 130 131 132 133 134 135 136
5.09782067 -28.12598902 27.19129812 11.78288346 26.47898083 -16.98185716 22.82117735 15.20117735
137 138 139 140 141 142 143 144
132.50975356 37.24064430 -31.41296175 -28.28704887 -116.82236066 -11.40320912 9.81295113 6.37679088
145 146 147 148 149 150 151 152
-12.26184001 -11.26221641 -32.60379998 -20.51235366 -31.06838220 60.54506811 2.95983191 1.57184419
153 154 155 156 157 158 159 160
13.36374632 3.69020669 45.09678116 9.29238403 -16.96624686 11.45854427 46.26337673 -24.81397382
161 162 163 164 165 166 167 168
35.18505661 13.19184138 -21.38845136 -37.00734295 8.10320404 -115.36845136 -45.23679596 -46.78845136
169 170 171 172 173 174 175 176
-46.69949036 16.30937683 12.74491980 0.43199840 18.96893236 -10.28349486 22.97029650 -9.13948231
177 178 179 180 181 182 183 184
31.21254751 10.22693332 -43.30830280 -19.58304812 -9.12570223 -0.10148134 3.93661588 37.98747693
185 186 187 188 189 190 191 192
25.62886534 71.14929591 42.89674671 27.65674671 62.30849266 61.60457970 36.59630223 68.76326752
193 194 195 196 197 198 199 200
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945 946 947 948 949 950 951 952
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[ reached 'max' / getOption("max.print") -- omitted 2626 entries ]residuals(micdol.lm, quantileFunction = qnorm) 1 2 3 4 5 6 7 8
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9 10 11 12 13 14 15 16
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17 18 19 20 21 22 23 24
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25 26 27 28 29 30 31 32
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33 34 35 36 37 38 39 40
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-8.88822222 -49.35477192 -11.79739109 -57.81052702 -56.64022222 -40.90000811 -6.35290964 -30.49193173
241 242 243 244 245 246 247 248
-37.72595842 -44.26117877 -80.64054064 -75.76449939 -12.27290686 -0.44392626 86.39591176 54.42154067
249 250 251 252 253 254 255 256
36.37433946 -37.57917316 21.66226705 16.67534613 26.36979806 14.65052884 15.09155886 8.60276034
257 258 259 260 261 262 263 264
12.98274918 10.56535361 11.65076034 26.21433946 51.32082684 39.08948927 39.59076034 7.05608251
265 266 267 268 269 270 271 272
3.65316299 -3.54317316 32.10448927 13.83048915 -35.86699239 30.98062006 12.83602878 -67.44951085
273 274 275 276 277 278 279 280
-39.15534884 -31.17766555 -11.21534884 9.17212665 0.13602878 30.34048915 -18.83534884 -10.85766555
281 282 283 284 285 286 287 288
62.85706362 75.55527775 -23.33315219 17.70963622 37.67225571 -53.41036378 -9.62115128 -48.61731153
289 290 291 292 293 294 295 296
84.29739092 34.58207738 14.84576984 -3.10264482 14.53125731 34.15875239 1.99036909 -3.41715735
297 298 299 300 301 302 303 304
4.85418450 28.20308663 32.62654700 -2.63337790 -2.23654789 14.78919531 37.43114197 60.00948657
305 306 307 308 309 310 311 312
53.56289943 25.80844756 7.54502652 -24.13724745 -28.28989832 -29.26650186 13.45858661 -60.21392497
313 314 315 316 317 318 319 320
-26.46955327 16.00148087 -65.74893839 -69.72527596 -66.48909858 2.08526888 7.21216838 -0.65418187
321 322 323 324 325 326 327 328
-4.18909320 12.52010573 55.70241918 16.78670223 -52.67445696 41.42760700 17.80188661 -57.75445696
329 330 331 332 333 334 335 336
21.01024450 55.90188661 58.18193664 62.97611592 37.68601534 20.07140042 19.13172857 -35.61586862
337 338 339 340 341 342 343 344
97.48975701 75.27368542 30.13831066 99.38990534 32.98373022 -27.44885165 6.10728576 -40.14885165
345 346 347 348 349 350 351 352
18.30204177 22.90941330 5.65683144 26.51296884 36.30537720 36.67296884 -11.37513572 -24.18732204
353 354 355 356 357 358 359 360
-6.29513572 -37.48419274 5.60991299 33.21105049 23.75154453 15.78086439 45.39392974 4.63898816
361 362 363 364 365 366 367 368
40.22365679 34.59264752 9.49973599 58.55318487 55.77943229 68.33683612 62.22361709 7.48455355
369 370 371 372 373 374 375 376
-4.29565415 11.18610759 -22.01126716 -14.70470108 -8.34753888 -30.74788528 12.29356564 53.00216775
377 378 379 380 381 382 383 384
-34.32120866 -19.64984710 -2.41808713 0.31876568 0.81823855 47.10172366 54.01673493 41.64348112
385 386 387 388 389 390 391 392
16.42902926 7.28576759 -35.78433299 -52.85687540 23.21843645 -23.36019444 -14.40159233 15.78376602
393 394 395 396 397 398 399 400
-12.08763187 -24.61821418 33.14324536 3.08376602 -12.08763187 23.40376602 -7.00763187 22.01136996
401 402 403 404 405 406 407 408
34.26701029 54.37511486 -3.40357077 84.43401827 118.16796918 106.86003313 81.36267063 73.07431275
409 410 411 412 413 414 415 416
54.84271449 -10.41784540 -21.03193952 -22.62055354 -17.07268776 9.19318873 -15.49784540 -22.30193952
417 418 419 420 421 422 423 424
1.65466848 10.73933964 10.86731224 -73.42055354 -24.82066036 -51.11601944 -60.77872758 37.57476137
425 426 427 428 429 430 431 432
38.75886709 48.58000459 24.64249864 5.74981849 33.45788384 13.91194227 45.61809470 61.61886709
433 434 435 436 437 438 439 440
47.31000459 30.99249864 36.22981849 24.56788384 1.30458595 40.43661752 41.40910771 162.04539041
441 442 443 444 445 446 447 448
-86.16809267 44.21715055 -18.36611704 25.73542291 43.54012437 -32.00415577 64.02704436 121.91579545
449 450 451 452 453 454 455 456
115.80390843 90.43121323 78.73942734 59.18350891 24.08465686 66.56578390 121.95327924 29.72601345
457 458 459 460 461 462 463 464
66.79833705 -9.19595176 4.12392924 28.82257843 41.78379756 -14.13562622 31.36389470 111.61092000
465 466 467 468 469 470 471 472
13.56638754 41.62333695 -28.33446292 -16.97004516 6.04340116 31.46333695 25.25953708 -0.03671183
473 474 475 476 477 478 479 480
-36.62859884 42.89333695 -17.28546292 -7.65671183 12.90140116 9.11870595 -13.94525281 -30.51671183
481 482 483 484 485 486 487 488
2.29938632 -38.06138093 -31.33432918 -49.06088539 -54.67702202 -24.62461368 -52.28932918 -20.20900104
489 490 491 492 493 494 495 496
-52.87088539 -32.66368868 -56.82725668 7.54871965 -26.20804760 -29.42932918 -40.52900104 -20.55922078
497 498 499 500 501 502 503 504
-25.56609515 -19.84216674 -27.30087483 -53.43764208 5.14774300 -50.04381321 -38.72661650 -13.77100963
505 506 507 508 509 510 511 512
-2.48710750 1.47068620 30.61151487 -121.37607266 -20.12100963 -23.64080183 46.35038758 0.05289250
513 514 515 516 517 518 519 520
-68.80264714 69.12214147 16.56289250 25.94392734 -2.68855436 54.20736772 4.98296014 -16.10974800
521 522 523 524 525 526 527 528
-1.99452597 -14.50433938 31.94003233 -31.37906179 -3.25272198 -18.22244439 38.60894159 22.66749694
529 530 531 532 533 534 535 536
31.32883477 18.75680736 -2.52835028 23.36894159 8.90916361 33.03164972 50.99255561 58.92894159
537 538 539 540 541 542 543 544
15.89416361 -45.72825384 -5.92806446 27.64697392 -13.34240089 29.98282113 -28.24250771 -7.72214096
545 546 547 548 549 550 551 552
-3.41373507 15.10598424 -2.52879374 2.74587742 -11.50027349 -1.64730762 15.29759826 -2.03901576
553 554 555 556 557 558 559 560
19.48453960 7.82587742 -4.74614998 -12.78567933 28.15535904 7.67759826 6.85098424 12.28787293
561 562 563 564 565 566 567 568
3.73972651 23.40399496 -16.89361141 62.99040114 33.06909762 16.69483554 7.85038038 8.48780057
569 570 571 572 573 574 575 576
49.31199496 -16.89361141 25.75980057 -0.64133837 29.35485210 26.77019811 21.44017994 18.41846719
577 578 579 580 581 582 583 584
11.62595669 -1.43256904 -2.71802161 7.85038038 12.93280057 1.05199496 -6.73361141 -27.41746712
585 586 587 588 589 590 591 592
-4.10535634 1.14692288 11.17671667 -0.66183113 -2.30961962 -41.89342834 35.12733554 35.42829112
593 594 595 596 597 598 599 600
21.18097442 19.66664757 12.85426002 -33.23033125 -6.83587089 31.61829112 39.59597442 -44.08283617
601 602 603 604 605 606 607 608
21.87070359 5.23426002 9.16891772 305.93829112 33.24597442 14.33716383 11.11176662 -0.21033125
609 610 611 612 613 614 615 616
-38.58587089 2.40829112 36.20930775 49.89716383 -25.13341233 -64.54104062 35.01625124 -0.07207900
617 618 619 620 621 622 623 624
3.19229271 -24.62541233 32.15986526 1.99625124 15.28161359 -25.30960171 -12.48985677 24.35895938
625 626 627 628 629 630 631 632
-0.39852674 6.02329574 23.36799819 11.22040982 -0.69331535 7.28414420 -0.18571155 2.66314359
633 634 635 636 637 638 639 640
35.00966485 11.58326696 12.38768465 6.01414420 7.43428845 3.56974920 -1.97054401 -2.86670426
641 642 643 644 645 646 647 648
31.83466485 -13.34877531 26.25611063 -3.79257943 -13.98281127 -21.44145880 -1.07226205 17.52782499
649 650 651 652 653 654 655 656
35.90811063 -25.56400800 8.45385540 -24.40479213 -1.77617501 19.64275765 -2.55281127 10.30854120
657 658 659 660 661 662 663 664
9.08773795 64.64173732 2.04811247 80.14445604 -72.74205581 -95.68795008 -65.35431854 -1.69699868
665 666 667 668 669 670 671 672
32.90579371 4.46898264 -24.47832857 3.16121946 -23.99258380 -8.59123133 -42.43701489 -8.03567897
673 674 675 676 677 678 679 680
-25.15566190 -11.44378054 6.48741620 -26.37123133 20.66796541 -2.95567897 -20.45666190 40.62621946
681 682 683 684 685 686 687 688
-25.68591714 -23.83123133 -27.19701489 -11.85548483 -35.10727395 48.49117661 -14.78727395 -17.84414348
689 690 691 692 693 694 695 696
21.18617661 13.93940022 19.54158953 -24.94727395 31.05085652 53.30940022 23.35158953 -8.18327395
697 698 699 700 701 702 703 704
8.82585652 4.46450994 27.06273356 23.03107881 -5.43507714 -21.34203734 -4.69137076 -32.56727395
705 706 707 708 709 710 711 712
-1.92681014 14.90673217 17.74940022 14.32250738 4.72492286 22.68462933 -4.69137076 9.94227923
713 714 715 716 717 718 719 720
-6.72831908 -51.88582233 7.20694085 -31.96772077 -59.41703703 -9.95438744 -31.96084656 6.90243301
721 722 723 724 725 726 727 728
-0.00944136 -44.13301295 -31.48338771 -59.82148829 -36.28810321 -38.13965942 -19.50740697 32.30647122
729 730 731 732 733 734 735 736
5.49865126 4.93001282 14.86431248 19.57672084 35.36063622 -31.42609640 43.37418515 26.85873418
737 738 739 740 741 742 743 744
15.32115232 15.81084721 -2.40531028 10.40969808 27.67528013 56.34208315 13.41156689 -4.70616296
745 746 747 748 749 750 751 752
-39.07563266 80.83178548 53.42792288 103.86033124 -19.00590238 5.57585025 28.08521937 31.05136537
753 754 755 756 757 758 759 760
11.30484296 0.63001050 30.21385025 50.94521937 30.69382147 33.40323917 4.44001050 16.24385025
761 762 763 764 765 766 767 768
-18.27980501 -9.43889213 52.30631673 10.08233083 33.71824148 -3.88766917 26.76949106 -10.83536811
769 770 771 772 773 774 775 776
32.74418593 55.02871059 24.48025752 46.96818593 56.44281117 27.08871059 33.02253946 11.35692419
777 778 779 780 781 782 783 784
31.05085260 53.64881117 36.40204392 18.69442382 53.34253946 18.77418593 47.29881117 49.94871059
785 786 787 788 789 790 791 792
45.88076234 43.18253946 35.44973617 48.86666525 -10.00619435 12.85380565 105.22115478 81.38455123
793 794 795 796 797 798 799 800
127.91963970 -51.92429791 -4.20992621 9.94097968 61.05736566 49.58823456 11.42622487 115.46474416
801 802 803 804 805 806 807 808
49.41075164 52.12823456 44.41195690 74.81075164 69.81195690 116.53615837 7.90973749 -13.97377513
809 810 811 812 813 814 815 816
-56.05011270 12.75823456 31.71195690 24.19005711 22.60080858 21.72978919 5.78027932 6.82379879
817 818 819 820 821 822 823 824
-6.58945755 6.70595147 8.55031344 34.57427769 31.69541518 -23.83477092 -4.02947128 -18.04487917
825 826 827 828 829 830 831 832
46.09017196 24.41427769 26.80790923 25.69522908 3.14802148 -0.98572231 47.36017196 31.39927769
833 834 835 836 837 838 839 840
14.10790923 26.96522908 0.06329443 -2.82412604 11.34121235 10.47311078 46.18430752 43.80565999
841 842 843 844 845 846 847 848
52.74485673 23.49934605 -24.21878765 35.87311078 41.27934605 -21.87412604 33.33311078 51.42565999
849 850 851 852 853 854 855 856
-32.92842663 -39.53918042 -21.38534067 12.53796389 -29.45661842 -11.86949221 -3.46434796 12.99111279
857 858 859 860 861 862 863 864
-23.61509330 -31.49584709 10.50565204 -31.54534067 46.33602844 -44.86595175 38.07830332 27.21943035
865 866 867 868 869 870 871 872
54.38470347 68.45032327 71.67579840 121.11933816 43.05795361 40.56714299 38.75151469 41.47242058
873 874 875 876 877 878 879 880
9.13166370 24.34941319 43.82203307 59.19000567 41.42921549 56.34416890 22.70650529 -25.71910109
881 882 883 884 885 886 887 888
-40.72530973 3.87797752 -4.25508853 9.13934586 29.50489071 -20.47349471 -15.55910109 -12.78530973
889 890 891 892 893 894 895 896
-25.33202248 -6.79508853 14.21934586 14.26489071 8.73650529 -8.95510109 -9.39864306 -2.47202248
897 898 899 900 901 902 903 904
-7.08305871 -13.03768910 -54.76349471 -89.21910109 -24.21530973 -14.70305871 26.72911638 16.71030579
905 906 907 908 909 910 911 912
10.14740199 -1.56856691 16.56911638 19.25030579 29.32384555 31.45143309 59.89030579 49.64384555
913 914 915 916 917 918 919 920
-34.22177686 -50.16255250 -46.72272184 -50.52924794 -45.70316358 -10.80170404 -41.13155979 -48.80609903
921 922 923 924 925 926 927 928
-31.93270464 -38.47008184 -27.99177879 7.33752901 24.48936135 9.65633343 -5.79477848 -6.56432276
929 930 931 932 933 934 935 936
-7.31564322 -23.53059820 -11.89542950 -9.68341517 -27.82244546 16.22752901 17.88536135 -6.67782042
937 938 939 940 941 942 943 944
-0.89620705 22.99204087 -39.83542950 -4.09247099 9.88436135 -29.02982042 -19.94620705 -49.73254328
945 946 947 948 949 950 951 952
-15.27141517 16.20422121 39.08752901 11.78936135 11.80379295 8.75100547 26.47898167 42.61936797
953 954 955 956 957 958 959 960
36.62352998 26.90587993 9.00097411 1.47594244 8.16188643 14.77216467 -2.69507145 2.67728479
961 962 963 964 965 966 967 968
49.44482900 63.96014129 66.57994623 55.98883651 47.04651509 48.46769248 22.82406561 46.09563871
969 970 971 972 973 974 975 976
21.33187505 60.00031173 34.34837514 34.86855731 42.81296397 49.83326133 14.46284208 32.98995861
977 978 979 980 981 982 983 984
18.13024591 -15.92297312 -7.22476680 -13.17703299 24.89800883 20.62950136 21.41499909 20.09671029
985 986 987 988 989 990 991 992
19.04735241 0.18075414 9.54059129 24.90664715 43.35166858 56.77604028 9.33194616 -5.53394058
993 994 995 996 997 998 999 1000
9.15855417 14.43322532 8.39262649 9.71207442 11.55254065 -45.49371218 -2.47065030 12.39943644
[ reached 'max' / getOption("max.print") -- omitted 2626 entries ]plot(micdol.lm)
Post-hoc comparisons
micdol.emm <- emmeans(micdol.lm, ~ begin_date_year*age_group)
pairs(micdol.emm, simple = "age_group")begin_date_year = 1993:
contrast estimate SE df t.ratio p.value
age_group0 - age_group1 -53.4 6.24 3601 -8.556 <.0001
age_group0 - age_group2 -124.1 6.13 3601 -20.229 <.0001
age_group0 - age_group3 -186.4 6.14 3601 -30.385 <.0001
age_group0 - age_group4 -242.8 6.15 3601 -39.448 <.0001
age_group0 - age_group5 -285.2 6.22 3601 -45.862 <.0001
age_group0 - age_group6 -318.1 6.28 3601 -50.657 <.0001
age_group0 - age_group7 -343.8 6.39 3601 -53.815 <.0001
age_group0 - age_group8 -364.3 6.54 3601 -55.690 <.0001
age_group0 - age_group9 -384.4 6.87 3601 -55.927 <.0001
age_group0 - age_group10 -402.2 7.47 3601 -53.817 <.0001
age_group1 - age_group2 -70.7 2.59 3601 -27.324 <.0001
age_group1 - age_group3 -133.1 2.57 3601 -51.875 <.0001
age_group1 - age_group4 -189.4 2.61 3601 -72.476 <.0001
age_group1 - age_group5 -231.8 2.74 3601 -84.527 <.0001
age_group1 - age_group6 -264.7 2.87 3601 -92.172 <.0001
age_group1 - age_group7 -290.5 3.10 3601 -93.724 <.0001
age_group1 - age_group8 -310.9 3.40 3601 -91.422 <.0001
age_group1 - age_group9 -331.0 3.99 3601 -82.868 <.0001
age_group1 - age_group10 -348.8 4.96 3601 -70.293 <.0001
age_group2 - age_group3 -62.4 2.24 3601 -27.901 <.0001
age_group2 - age_group4 -118.7 2.29 3601 -51.820 <.0001
age_group2 - age_group5 -161.1 2.43 3601 -66.279 <.0001
age_group2 - age_group6 -194.0 2.57 3601 -75.407 <.0001
age_group2 - age_group7 -219.8 2.82 3601 -77.821 <.0001
age_group2 - age_group8 -240.2 3.15 3601 -76.252 <.0001
age_group2 - age_group9 -260.3 3.78 3601 -68.825 <.0001
age_group2 - age_group10 -278.1 4.80 3601 -57.976 <.0001
age_group3 - age_group4 -56.3 2.25 3601 -25.043 <.0001
age_group3 - age_group5 -98.8 2.39 3601 -41.359 <.0001
age_group3 - age_group6 -131.7 2.53 3601 -52.043 <.0001
age_group3 - age_group7 -157.4 2.78 3601 -56.556 <.0001
age_group3 - age_group8 -177.9 3.11 3601 -57.142 <.0001
age_group3 - age_group9 -198.0 3.75 3601 -52.797 <.0001
age_group3 - age_group10 -215.8 4.77 3601 -45.209 <.0001
age_group4 - age_group5 -42.4 2.44 3601 -17.380 <.0001
age_group4 - age_group6 -75.3 2.58 3601 -29.203 <.0001
age_group4 - age_group7 -101.1 2.83 3601 -35.733 <.0001
age_group4 - age_group8 -121.5 3.15 3601 -38.545 <.0001
age_group4 - age_group9 -141.6 3.78 3601 -37.434 <.0001
age_group4 - age_group10 -159.4 4.80 3601 -33.218 <.0001
age_group5 - age_group6 -32.9 2.70 3601 -12.208 <.0001
age_group5 - age_group7 -58.7 2.93 3601 -19.988 <.0001
age_group5 - age_group8 -79.1 3.25 3601 -24.361 <.0001
age_group5 - age_group9 -99.2 3.86 3601 -25.691 <.0001
age_group5 - age_group10 -117.0 4.86 3601 -24.063 <.0001
age_group6 - age_group7 -25.7 3.05 3601 -8.444 <.0001
age_group6 - age_group8 -46.2 3.35 3601 -13.791 <.0001
age_group6 - age_group9 -66.3 3.95 3601 -16.793 <.0001
age_group6 - age_group10 -84.1 4.93 3601 -17.050 <.0001
age_group7 - age_group8 -20.5 3.54 3601 -5.773 <.0001
age_group7 - age_group9 -40.5 4.11 3601 -9.861 <.0001
age_group7 - age_group10 -58.3 5.06 3601 -11.523 <.0001
age_group8 - age_group9 -20.1 4.34 3601 -4.630 0.0002
age_group8 - age_group10 -37.9 5.25 3601 -7.216 <.0001
age_group9 - age_group10 -17.8 5.65 3601 -3.150 0.0618
P value adjustment: tukey method for comparing a family of 11 estimates test(pairs(micdol.emm, by = "begin_date_year"), by = NULL, adjust = "mvt") contrast begin_date_year estimate SE df t.ratio p.value
age_group0 - age_group1 1993 -53.4 6.24 3601 -8.556 <.0001
age_group0 - age_group2 1993 -124.1 6.13 3601 -20.229 <.0001
age_group0 - age_group3 1993 -186.4 6.14 3601 -30.385 <.0001
age_group0 - age_group4 1993 -242.8 6.15 3601 -39.448 <.0001
age_group0 - age_group5 1993 -285.2 6.22 3601 -45.862 <.0001
age_group0 - age_group6 1993 -318.1 6.28 3601 -50.657 <.0001
age_group0 - age_group7 1993 -343.8 6.39 3601 -53.815 <.0001
age_group0 - age_group8 1993 -364.3 6.54 3601 -55.690 <.0001
age_group0 - age_group9 1993 -384.4 6.87 3601 -55.927 <.0001
age_group0 - age_group10 1993 -402.2 7.47 3601 -53.817 <.0001
age_group1 - age_group2 1993 -70.7 2.59 3601 -27.324 <.0001
age_group1 - age_group3 1993 -133.1 2.57 3601 -51.875 <.0001
age_group1 - age_group4 1993 -189.4 2.61 3601 -72.476 <.0001
age_group1 - age_group5 1993 -231.8 2.74 3601 -84.527 <.0001
age_group1 - age_group6 1993 -264.7 2.87 3601 -92.172 <.0001
age_group1 - age_group7 1993 -290.5 3.10 3601 -93.724 <.0001
age_group1 - age_group8 1993 -310.9 3.40 3601 -91.422 <.0001
age_group1 - age_group9 1993 -331.0 3.99 3601 -82.868 <.0001
age_group1 - age_group10 1993 -348.8 4.96 3601 -70.293 <.0001
age_group2 - age_group3 1993 -62.4 2.24 3601 -27.901 <.0001
age_group2 - age_group4 1993 -118.7 2.29 3601 -51.820 <.0001
age_group2 - age_group5 1993 -161.1 2.43 3601 -66.279 <.0001
age_group2 - age_group6 1993 -194.0 2.57 3601 -75.407 <.0001
age_group2 - age_group7 1993 -219.8 2.82 3601 -77.821 <.0001
age_group2 - age_group8 1993 -240.2 3.15 3601 -76.252 <.0001
age_group2 - age_group9 1993 -260.3 3.78 3601 -68.825 <.0001
age_group2 - age_group10 1993 -278.1 4.80 3601 -57.976 <.0001
age_group3 - age_group4 1993 -56.3 2.25 3601 -25.043 <.0001
age_group3 - age_group5 1993 -98.8 2.39 3601 -41.359 <.0001
age_group3 - age_group6 1993 -131.7 2.53 3601 -52.043 <.0001
age_group3 - age_group7 1993 -157.4 2.78 3601 -56.556 <.0001
age_group3 - age_group8 1993 -177.9 3.11 3601 -57.142 <.0001
age_group3 - age_group9 1993 -198.0 3.75 3601 -52.797 <.0001
age_group3 - age_group10 1993 -215.8 4.77 3601 -45.209 <.0001
age_group4 - age_group5 1993 -42.4 2.44 3601 -17.380 <.0001
age_group4 - age_group6 1993 -75.3 2.58 3601 -29.203 <.0001
age_group4 - age_group7 1993 -101.1 2.83 3601 -35.733 <.0001
age_group4 - age_group8 1993 -121.5 3.15 3601 -38.545 <.0001
age_group4 - age_group9 1993 -141.6 3.78 3601 -37.434 <.0001
age_group4 - age_group10 1993 -159.4 4.80 3601 -33.218 <.0001
age_group5 - age_group6 1993 -32.9 2.70 3601 -12.208 <.0001
age_group5 - age_group7 1993 -58.7 2.93 3601 -19.988 <.0001
age_group5 - age_group8 1993 -79.1 3.25 3601 -24.361 <.0001
age_group5 - age_group9 1993 -99.2 3.86 3601 -25.691 <.0001
age_group5 - age_group10 1993 -117.0 4.86 3601 -24.063 <.0001
age_group6 - age_group7 1993 -25.7 3.05 3601 -8.444 <.0001
age_group6 - age_group8 1993 -46.2 3.35 3601 -13.791 <.0001
age_group6 - age_group9 1993 -66.3 3.95 3601 -16.793 <.0001
age_group6 - age_group10 1993 -84.1 4.93 3601 -17.050 <.0001
age_group7 - age_group8 1993 -20.5 3.54 3601 -5.773 <.0001
age_group7 - age_group9 1993 -40.5 4.11 3601 -9.861 <.0001
age_group7 - age_group10 1993 -58.3 5.06 3601 -11.523 <.0001
age_group8 - age_group9 1993 -20.1 4.34 3601 -4.630 0.0001
age_group8 - age_group10 1993 -37.9 5.25 3601 -7.216 <.0001
age_group9 - age_group10 1993 -17.8 5.65 3601 -3.150 0.0525
P value adjustment: mvt method for 55 tests #export tables
# #interpret(eta_squared(micdol.lm), rules = "cohen1992") %>%
# write.csv(file = "Outputs/Tables/micdol_eta_squared.csv")
#slope contrasts
# Obtain slope estimates
micdol.slopes <- emtrends(micdol.lm, ~age_group, var = "begin_date_year")
# Compare estimated slopes to 0
micdol.slope.contrasts <- test(micdol.slopes) %>%
mutate(Species = "Smallmouth Bass") %>%
rename(Age = age_group)
micdol.slope.contrasts %>%
write.csv(file = "Outputs/Tables/micdol_emmeans.csv")Plot raw data
(micdol.length.year.plot <- ggplot(data = micdol %>%
mutate(Age = as.factor(age_group)),
aes(x = begin_date_year, y = length_mean_mm,
color = Age, fill = Age, group = Age))+
#geom_point()+
geom_smooth(method = "lm")+
stat_poly_eq(use_label("eq"))+
scale_color_viridis_d()+
scale_fill_viridis_d()+
labs(y = "Length (mm)", x = "Year")+
theme_bw()
)`geom_smooth()` using formula = 'y ~ x'
Marginal Effects
result <- predict_response(micdol.lm, c("begin_date_year", "age_group"))
# plot(result)
# test_predictions(result) %>%
# write.csv(file = "Outputs/Tables/micdol_pairwise_length_time_slopes.csv", row.names = F)
(micdol.marginal.plot <- ggpredict(micdol.lm, terms = c("begin_date_year", "age_group")) %>%
plot()+
#stat_poly_eq(use_label("eq"))+
stat_regline_equation()+
stat_poly_line()+
# annotate(geom = "text", label = "y = 77 + 0.07x", x = 2000, y = 224)+
# annotate(geom = "text", label = "y = 21 + 0.093x", x = 2000, y = 212)+
# annotate(geom = "text", label = "y = -64 + 0.13x", x = 2000, y = 199)+
# annotate(geom = "text", label = "y = 230 - 0.028x", x = 2000, y = 182)+
# annotate(geom = "text", label = "y = 610 - 0.23x", x = 2000, y = 160)+
# annotate(geom = "text", label = "y = 920 - 0.39x", x = 2000, y = 137)+
# annotate(geom = "text", label = "y = 1200 - 0.55x", x = 2000, y = 110)+
# annotate(geom = "text", label = "y = 1700 - 0.83x", x = 2000, y = 80)+
scale_color_viridis_d()+
scale_fill_viridis_d()+
labs(title = NULL,
y = "Mean Length-at-Age (mm)",
x = "Year")+
guides(fill = guide_legend(title = "Age", reverse = T), color = guide_legend(title = "Age", reverse = T))+
theme_bw()
)Scale for colour is already present.
Adding another scale for colour, which will replace the existing scale.ggsave(filename = "~/GitHub/EAGER_growth/Outputs/Figures/Linear Models/micdol_marginal_effects_plot.tiff",
micdol.marginal.plot,
dpi = 300, width = 200, height = 150, units = "mm")
Walleye
Model
#filter data for only Black Crappie, Pomooxis nigromaculatus
sanvit <- all.grow.merge %>% filter(species == "walleye") %>%
filter(age_group %in% c(0:12), #match age groups in BRTs
!is.na(age_group), !is.na(begin_date_year), !is.na(length_mean_mm),
!is.na(log_max_depth), !is.na(logarea))
#linear model
sanvit.lm <- lm(length_mean_mm ~ begin_date_year*age_group + log_max_depth + logarea + doy, data = sanvit)
summary(sanvit.lm)
Call:
lm(formula = length_mean_mm ~ begin_date_year * age_group + log_max_depth +
logarea + doy, data = sanvit)
Residuals:
Min 1Q Median 3Q Max
-224.917 -34.739 -2.713 33.363 286.702
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 1.445e+03 1.142e+03 1.265 0.2059
begin_date_year -6.604e-01 5.744e-01 -1.150 0.2503
age_group1 4.367e+02 1.219e+03 0.358 0.7202
age_group2 -6.522e+02 1.202e+03 -0.542 0.5875
age_group3 -1.191e+03 1.196e+03 -0.996 0.3194
age_group4 -1.409e+03 1.200e+03 -1.175 0.2401
age_group5 -1.237e+03 1.200e+03 -1.031 0.3025
age_group6 -1.724e+03 1.204e+03 -1.432 0.1522
age_group7 -1.101e+03 1.208e+03 -0.911 0.3624
age_group8 -2.895e+02 1.221e+03 -0.237 0.8126
age_group9 1.985e+02 1.247e+03 0.159 0.8736
age_group10 1.479e+03 1.270e+03 1.165 0.2442
age_group11 1.714e+02 1.371e+03 0.125 0.9006
age_group12 1.348e+03 1.493e+03 0.903 0.3667
log_max_depth 1.026e+01 1.349e+00 7.609 3.41e-14 ***
logarea -4.011e+00 6.119e-01 -6.555 6.28e-11 ***
doy 1.769e-01 1.628e-02 10.862 < 2e-16 ***
begin_date_year:age_group1 -1.842e-01 6.131e-01 -0.301 0.7638
begin_date_year:age_group2 4.063e-01 6.045e-01 0.672 0.5015
begin_date_year:age_group3 7.070e-01 6.013e-01 1.176 0.2398
begin_date_year:age_group4 8.420e-01 6.031e-01 1.396 0.1628
begin_date_year:age_group5 7.757e-01 6.034e-01 1.286 0.1987
begin_date_year:age_group6 1.039e+00 6.054e-01 1.716 0.0862 .
begin_date_year:age_group7 7.396e-01 6.076e-01 1.217 0.2235
begin_date_year:age_group8 3.445e-01 6.137e-01 0.561 0.5746
begin_date_year:age_group9 1.072e-01 6.268e-01 0.171 0.8642
begin_date_year:age_group10 -5.249e-01 6.380e-01 -0.823 0.4107
begin_date_year:age_group11 1.328e-01 6.887e-01 0.193 0.8471
begin_date_year:age_group12 -4.456e-01 7.488e-01 -0.595 0.5518
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 56.45 on 4073 degrees of freedom
(96 observations deleted due to missingness)
Multiple R-squared: 0.8082, Adjusted R-squared: 0.8069
F-statistic: 613 on 28 and 4073 DF, p-value: < 2.2e-16#calculate and interpret effect sizes
eta_squared(sanvit.lm) begin_date_year age_group log_max_depth logarea
0.001469746 0.793872670 0.002148514 0.002735030
doy begin_date_year:age_group
0.005694827 0.002298081 #interpret(eta_squared(sanvit.lm), rules = "cohen1992")
#calculate AIC score
AIC(sanvit.lm)[1] 44762.16#examine model fit
testDispersion(sanvit.lm)
DHARMa nonparametric dispersion test via sd of residuals fitted vs. simulated
data: simulationOutput
dispersion = 0.99355, p-value = 0.728
alternative hypothesis: two.sided
simulation.output <- simulateResiduals(fittedModel = sanvit.lm)
residuals(sanvit.lm) 1 2 3 4 5 6 7 8 9
35.5651827 15.7537501 -5.3113197 9.5139431 33.0375147 47.2878614 -19.5198264 -4.9959905 111.4970924
10 11 12 13 14 15 16 17 18
32.1970787 3.7595074 45.1352164 -20.9610030 38.7233092 4.0802143 55.9066430 1.0871326 112.2064448
19 20 21 22 23 24 25 26 27
103.8702807 28.5245321 58.3293074 41.5836222 -23.9688012 -37.7625707 -47.1151127 27.8778862 100.3199588
28 29 30 31 32 33 34 35 36
63.9894858 80.5079588 58.0838065 75.0299180 11.5001558 -1.9851534 79.6189588 47.2053822 58.0838065
37 38 39 40 41 42 43 44 45
8.1094858 -55.7800820 -81.2098442 101.8439588 29.4253822 53.8913177 -17.2905142 -41.1335664 -150.9460014
46 47 48 49 50 51 52 53 54
-224.9168892 4.0144055 -97.3585547 -17.5886962 1.7480260 55.8919645 -36.7591849 -49.6242145 39.4408151
55 56 57 58 59 60 61 62 63
31.1269645 25.3057855 19.1208151 29.9664955 -39.0382528 41.9986690 55.4887809 24.2700147 40.7800147
64 65 66 67 68 69 70 71 72
-16.3731158 116.7558171 -11.4132108 -0.5509365 -45.8717667 -101.7623982 -98.0315501 -103.3000266 -108.1123982
73 74 75 76 77 78 79 80 81
44.8030222 59.5563189 66.3449055 48.9515407 10.5743111 20.4358252 35.4684083 -19.2039589 -14.4901806
82 83 84 85 86 87 88 89 90
32.2194125 76.8915407 32.7993111 22.7684083 29.9027078 -18.7236988 67.5795028 52.7615407 7.3993111
91 92 93 94 95 96 97 98 99
11.7574918 -7.6948732 81.9715407 -104.3606889 14.2963012 49.7435091 -6.1212356 60.1405486 67.5235091
100 101 102 103 104 105 106 107 108
62.6805486 73.8735091 -3.6109599 -33.2145689 101.9884013 92.2985704 24.1672542 -26.4709599 -13.3692335
109 110 111 112 113 114 115 116 117
-35.3312356 92.2985704 -21.8359002 -54.3812356 52.5205486 72.6035091 34.3272542 -8.0559599 -11.6759002
118 119 120 121 122 123 124 125 126
-24.3245689 74.3088866 20.3524516 23.3736665 -21.4073926 -13.2200815 -54.7473895 33.3781024 -32.5725400
127 128 129 130 131 132 133 134 135
30.3426768 22.9762573 -24.3532071 -14.7039383 34.7076405 14.9113434 94.2992739 -50.3326173 -50.3344767
136 137 138 139 140 141 142 143 144
-90.3496936 -117.5483155 4.3603606 14.2235513 -53.0016573 -96.1039172 98.1393460 95.6488725 9.6143911
145 146 147 148 149 150 151 152 153
111.5201278 -0.9780854 23.0352361 93.5632634 -10.3630492 -16.2265604 122.0788321 36.6872338 25.1969508
154 155 156 157 158 159 160 161 162
-11.7174708 -23.7625108 92.5672338 118.6453357 91.2369508 108.2334396 -13.8314932 -53.1341446 63.5357923
163 164 165 166 167 168 169 170 171
75.3680189 11.8680189 110.0184028 66.7809847 -0.8368368 174.9259717 97.1415384 33.7050726 -8.6337012
172 173 174 175 176 177 178 179 180
22.1607676 58.0815415 67.3894344 27.2522428 133.3164681 -11.6840514 142.6191402 131.5348969 -37.3563195
181 182 183 184 185 186 187 188 189
2.1438764 125.2166380 -42.4205883 -4.9755362 9.3230231 -70.1931477 41.6645460 -16.8225200 87.2894627
190 191 192 193 194 195 196 197 198
90.7781819 40.0346227 15.2463142 5.7879188 66.0379942 54.8657487 32.2377583 -17.7069789 83.6494498
199 200 201 202 203 204 205 206 207
56.4110544 55.7011297 67.3888843 123.4449394 69.7461567 78.3925854 143.5880750 16.2584325 47.4400372
208 209 210 211 212 213 214 215 216
74.6701125 -35.3483281 -85.8848218 -158.8068848 -44.0811589 37.1090206 -65.4747920 -52.6503760 -91.7236355
217 218 219 220 221 222 223 224 225
-114.6714885 -14.2603990 -51.2811486 -20.8911784 6.3563946 -21.6512159 0.9150151 -2.9936580 29.8667677
226 227 228 229 230 231 232 233 234
51.6045830 76.3337987 -7.7078661 -10.9677629 -51.0820784 50.7349251 -34.1847075 45.1091283 -75.7794496
235 236 237 238 239 240 241 242 243
-37.8557045 11.8360813 3.7711411 6.9454425 -21.3457045 -56.3002522 -11.1857045 -10.1988589 32.1858057
244 245 246 247 248 249 250 251 252
28.5354425 -107.1002522 -64.9626276 18.3754425 68.3858648 -7.2576861 27.3921233 85.0345267 41.4475796
253 254 255 256 257 258 259 260 261
49.0539551 -2.8139253 2.5449603 -22.1703179 1.6616197 14.0802769 68.0431120 21.2744424 10.2439155
262 263 264 265 266 267 268 269 270
54.2944424 2.0031120 46.6744424 252.0776990 55.5433464 3.7807053 -40.7311266 42.8433464 82.3730917
271 272 273 274 275 276 277 278 279
-36.4463268 44.3261293 5.9639389 -20.8730716 -62.1881303 -72.1941616 -62.0961579 -58.9319192 -13.0309998
280 281 282 283 284 285 286 287 288
-82.8526127 -117.8602437 11.6072485 -33.3125031 -4.0155136 -52.8870723 -92.0592666 22.8512387 32.8343742
289 290 291 292 293 294 295 296 297
58.2246010 25.4075891 19.8987379 -36.8040686 -25.8185958 17.0818492 54.7459222 4.6716800 31.2646513
298 299 300 301 302 303 304 305 306
42.0541334 43.7740577 7.0348155 50.0557512 28.2547100 0.1789356 -25.6205375 133.1787077 -9.5126919
307 308 309 310 311 312 313 314 315
1.7451701 5.0181921 127.1007236 -1.6120228 156.0420929 60.4517432 81.3428919 65.5783239 85.8517432
316 317 318 319 320 321 322 323 324
-50.6219378 22.5867116 -24.6462906 6.4858616 -19.3654889 23.5715088 -10.9719286 40.4195943 11.5658616
325 326 327 328 329 330 331 332 333
66.9945111 45.1615088 37.8865635 48.6256018 -35.4126533 30.5235805 55.0569797 65.8205681 37.9535385
334 335 336 337 338 339 340 341 342
-4.6712495 79.0473881 110.5364137 13.7722564 65.8529436 57.9427014 17.8693513 -3.0943272 31.7217699
343 344 345 346 347 348 349 350 351
39.6806817 67.7774593 33.8597807 103.3277592 67.5101113 83.5939055 7.3939055 28.2241609 -40.4398887
352 353 354 355 356 357 358 359 360
-20.7334671 -52.5826657 -11.7974954 -73.7370414 -87.4657086 15.5668901 67.0638440 52.7835042 75.4839299
361 362 363 364 365 366 367 368 369
-0.7427250 1.0196784 52.9815549 -1.9927736 -25.5898880 54.5683108 -35.0651661 -13.0784127 -28.2678155
370 371 372 373 374 375 376 377 378
32.8567641 55.3698470 -106.6208551 123.7245096 69.7924600 43.5058525 78.7642542 13.9159092 83.7676627
379 380 381 382 383 384 385 386 387
47.7926913 57.5298017 -58.4792426 -45.0451996 1.5670748 -32.5981937 -35.6665290 -53.4236882 -11.1472826
388 389 390 391 392 393 394 395 396
48.5306411 56.2570192 37.4358641 -15.6000189 29.4905527 79.4358447 94.0321661 -15.9317367 122.9847521
397 398 399 400 401 402 403 404 405
6.7940999 31.7384533 144.7013873 -91.9699633 56.8003678 -4.6612442 68.9196040 -49.3788725 -79.8137195
406 407 408 409 410 411 412 413 414
-55.6837195 -17.5276382 -28.1008561 -108.6882886 -34.0270790 84.1249256 -12.2258561 -36.2982886 66.5979196
415 416 417 418 419 420 421 422 423
15.4923618 -33.4348561 -14.7082886 -35.9320790 -5.2561233 68.8849256 7.8071348 81.8776899 -30.7577387
424 425 426 427 428 429 430 431 432
31.1973264 60.0678620 -4.4070471 31.8287974 -19.5552904 -8.5555344 56.8470201 37.0923978 -2.9235707
433 434 435 436 437 438 439 440 441
-8.1995980 16.1267393 47.0837603 8.7667546 -39.6061497 48.9419647 48.8307656 57.4580124 104.7665480
442 443 444 445 446 447 448 449 450
74.2450915 38.6569736 15.3450637 0.4001546 -17.7623342 95.6291673 21.5795995 54.7436403 -42.7390009
451 452 453 454 455 456 457 458 459
-11.9540887 95.6291673 -20.8957782 6.4550637 44.8909991 35.9210420 3.3355392 -16.6046045 -32.3630212
460 461 462 463 464 465 466 467 468
26.8496134 9.4839799 25.5605392 40.2731499 0.1420153 -30.9401499 -14.3441317 70.9948179 4.1085950
469 470 471 472 473 474 475 476 477
40.4995507 -25.6074653 -25.3268053 7.3719501 -5.9270138 5.9449022 44.3416427 69.9683934 1.6720230
478 479 480 481 482 483 484 485 486
-20.4821362 -26.2157013 42.1942356 -110.7655450 -78.6024285 2.0783940 22.1012792 83.7502447 145.6472744
487 488 489 490 491 492 493 494 495
-38.3163217 119.7096751 60.4517651 132.2137987 29.5774920 58.3321339 -94.7877629 39.1686846 -0.5235252
496 497 498 499 500 501 502 503 504
-12.4048758 54.3162150 6.3491874 20.1186846 52.8164748 15.5351242 31.4562150 28.5263406 38.4359475
505 506 507 508 509 510 511 512 513
14.4737940 49.8659475 54.8618979 43.7663406 65.2737940 -18.0145909 -25.3817522 46.6687834 19.5754500
514 515 516 517 518 519 520 521 522
40.6138743 30.1873600 25.0787834 21.2802408 -16.5532448 -41.9124003 5.3413544 -27.3545334 6.5669810
523 524 525 526 527 528 529 530 531
47.2478255 18.3427377 14.4810481 87.4364258 4.0713544 36.7518900 21.8478255 -13.7740064 16.7713544
532 533 534 535 536 537 538 539 540
-26.7195334 6.6078255 47.5010481 6.9616566 35.8770646 60.4099098 59.4423343 11.3875279 93.4299098
541 542 543 544 545 546 547 548 549
56.0556676 89.6423176 54.0736390 70.7876676 38.8423176 30.5786390 91.9680712 36.9246305 13.2884597
550 551 552 553 554 555 556 557 558
51.3280712 42.9217931 -1.4305132 9.9324207 12.2582516 15.4745375 33.9939266 -32.9583030 -78.5492058
559 560 561 562 563 564 565 566 567
14.5786870 -19.2877947 -46.4381113 -97.9485045 -46.0668442 -11.7260734 -10.0983030 -4.0477947 -56.2268442
568 569 570 571 572 573 574 575 576
14.9439266 -19.4116363 -153.1181113 8.5939266 -34.2283030 -68.3892058 -115.4415730 13.6739266 -5.0183030
577 578 579 580 581 582 583 584 585
-9.5458725 -74.5899063 -69.2413130 -93.3711280 -65.7421113 -86.5185045 -116.4199286 -133.6968442 -9.1058207
586 587 588 589 590 591 592 593 594
5.6305932 -2.7323030 17.7972110 -37.2164786 -71.9229063 -48.6390907 -34.8906518 -76.0714447 -22.3835045
595 596 597 598 599 600 601 602 603
-59.2699286 -96.0201775 -26.7374881 -1.0336339 21.8161492 -51.7374727 -38.8086279 -1.0336339 -18.8238508
604 605 606 607 608 609 610 611 612
-65.7074727 -28.3445109 -28.9717745 -1.0438508 -9.5986279 115.5921033 -25.5603627 -7.2490251 -8.7887361
613 614 615 616 617 618 619 620 621
-1.4506932 27.0409749 60.0452639 79.8293068 -25.5603627 27.4643082 42.9002639 -15.1767193 27.3878038
622 623 624 625 626 627 628 629 630
12.5490017 8.2031704 26.1199124 -48.7996113 -61.9000095 31.4518038 28.2448991 40.7998371 -54.9023331
631 632 633 634 635 636 637 638 639
-33.1362779 20.0218038 0.7282325 1.9899124 -40.6298448 -2.0450479 67.8075625 -36.3692278 -36.4442104
640 641 642 643 644 645 646 647 648
-58.5085556 -73.9532892 -68.8197918 -26.3168800 -9.5752534 52.2358794 36.4510217 64.9769537 70.6243432
649 650 651 652 653 654 655 656 657
84.3005742 -14.7221113 0.9495839 -3.4378486 -29.1066390 86.7583596 -13.5513099 -17.2621113 15.3429172
658 659 660 661 662 663 664 665 666
-3.7553486 -24.9493608 -13.4201623 4.4991240 35.7817421 43.1894008 35.1244427 67.8724734 -11.2967240
667 668 669 670 671 672 673 674 675
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721 722 723 724 725 726 727 728 729
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739 740 741 742 743 744 745 746 747
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748 749 750 751 752 753 754 755 756
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811 812 813 814 815 816 817 818 819
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820 821 822 823 824 825 826 827 828
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829 830 831 832 833 834 835 836 837
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838 839 840 841 842 843 844 845 846
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847 848 849 850 851 852 853 854 855
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865 866 867 868 869 870 871 872 873
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874 875 876 877 878 879 880 881 882
17.6024435 4.7224876 -24.6426044 20.0358914 10.4987190 18.1487566 14.2794148 -3.6095236 4.9024435
883 884 885 886 887 888 889 890 891
27.5824876 -7.2859378 1.6910614 -23.1441086 -49.1912810 4.1194148 26.4471431 -16.6492434 -8.5512810
892 893 894 895 896 897 898 899 900
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910 911 912 913 914 915 916 917 918
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919 920 921 922 923 924 925 926 927
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928 929 930 931 932 933 934 935 936
32.0924284 27.3075193 14.2250305 78.4532760 -7.5350802 -53.1130358 -24.6884473 8.4692631 -29.8094607
937 938 939 940 941 942 943 944 945
5.5755342 19.4465165 -5.5555230 -41.4785426 -54.7472143 -68.7388212 -73.7392677 -18.7156308 27.2586744
946 947 948 949 950 951 952 953 954
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955 956 957 958 959 960 961 962 963
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964 965 966 967 968 969 970 971 972
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973 974 975 976 977 978 979 980 981
-98.2542458 -91.4288201 -111.4663078 -107.3331843 -120.5738563 -97.8426714 -48.6328025 286.7019697 112.7119697
982 983 984 985 986 987 988 989 990
111.4419697 -49.6892163 -36.9910757 -36.9910757 34.1187074 16.8684188 8.5459303 60.1580473 -18.1530478
991 992 993 994 995 996 997 998 999
21.3794838 -18.0077266 -29.0306928 45.0696246 67.9704691 41.6053813 44.6590695 47.1693072 -65.2952136
1000
-7.8501227
[ reached 'max' / getOption("max.print") -- omitted 3102 entries ]residuals(sanvit.lm, quantileFunction = qnorm) 1 2 3 4 5 6 7 8 9
35.5651827 15.7537501 -5.3113197 9.5139431 33.0375147 47.2878614 -19.5198264 -4.9959905 111.4970924
10 11 12 13 14 15 16 17 18
32.1970787 3.7595074 45.1352164 -20.9610030 38.7233092 4.0802143 55.9066430 1.0871326 112.2064448
19 20 21 22 23 24 25 26 27
103.8702807 28.5245321 58.3293074 41.5836222 -23.9688012 -37.7625707 -47.1151127 27.8778862 100.3199588
28 29 30 31 32 33 34 35 36
63.9894858 80.5079588 58.0838065 75.0299180 11.5001558 -1.9851534 79.6189588 47.2053822 58.0838065
37 38 39 40 41 42 43 44 45
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46 47 48 49 50 51 52 53 54
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55 56 57 58 59 60 61 62 63
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64 65 66 67 68 69 70 71 72
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73 74 75 76 77 78 79 80 81
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82 83 84 85 86 87 88 89 90
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91 92 93 94 95 96 97 98 99
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100 101 102 103 104 105 106 107 108
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109 110 111 112 113 114 115 116 117
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118 119 120 121 122 123 124 125 126
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127 128 129 130 131 132 133 134 135
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136 137 138 139 140 141 142 143 144
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154 155 156 157 158 159 160 161 162
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163 164 165 166 167 168 169 170 171
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172 173 174 175 176 177 178 179 180
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181 182 183 184 185 186 187 188 189
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190 191 192 193 194 195 196 197 198
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199 200 201 202 203 204 205 206 207
56.4110544 55.7011297 67.3888843 123.4449394 69.7461567 78.3925854 143.5880750 16.2584325 47.4400372
208 209 210 211 212 213 214 215 216
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217 218 219 220 221 222 223 224 225
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226 227 228 229 230 231 232 233 234
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235 236 237 238 239 240 241 242 243
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244 245 246 247 248 249 250 251 252
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253 254 255 256 257 258 259 260 261
49.0539551 -2.8139253 2.5449603 -22.1703179 1.6616197 14.0802769 68.0431120 21.2744424 10.2439155
262 263 264 265 266 267 268 269 270
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271 272 273 274 275 276 277 278 279
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280 281 282 283 284 285 286 287 288
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289 290 291 292 293 294 295 296 297
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298 299 300 301 302 303 304 305 306
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307 308 309 310 311 312 313 314 315
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316 317 318 319 320 321 322 323 324
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325 326 327 328 329 330 331 332 333
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334 335 336 337 338 339 340 341 342
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343 344 345 346 347 348 349 350 351
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352 353 354 355 356 357 358 359 360
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361 362 363 364 365 366 367 368 369
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370 371 372 373 374 375 376 377 378
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388 389 390 391 392 393 394 395 396
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397 398 399 400 401 402 403 404 405
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406 407 408 409 410 411 412 413 414
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415 416 417 418 419 420 421 422 423
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424 425 426 427 428 429 430 431 432
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433 434 435 436 437 438 439 440 441
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496 497 498 499 500 501 502 503 504
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505 506 507 508 509 510 511 512 513
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514 515 516 517 518 519 520 521 522
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523 524 525 526 527 528 529 530 531
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532 533 534 535 536 537 538 539 540
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541 542 543 544 545 546 547 548 549
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550 551 552 553 554 555 556 557 558
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559 560 561 562 563 564 565 566 567
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577 578 579 580 581 582 583 584 585
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613 614 615 616 617 618 619 620 621
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622 623 624 625 626 627 628 629 630
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631 632 633 634 635 636 637 638 639
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658 659 660 661 662 663 664 665 666
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694 695 696 697 698 699 700 701 702
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703 704 705 706 707 708 709 710 711
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712 713 714 715 716 717 718 719 720
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721 722 723 724 725 726 727 728 729
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730 731 732 733 734 735 736 737 738
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739 740 741 742 743 744 745 746 747
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748 749 750 751 752 753 754 755 756
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-2.9538021 -31.0027404 -0.9007734 -21.9791546 -75.2321554 -70.8573255 18.2722064 -30.8938021 0.7472596
766 767 768 769 770 771 772 773 774
-20.7091546 -32.0521554 -73.3973255 70.7355021 -8.3924480 12.3455397 -67.0361476 -65.7104590 -2.1358638
775 776 777 778 779 780 781 782 783
-32.4276067 -49.5949701 -34.7383291 -18.5403761 -7.5380132 -37.2815328 -20.9912405 -4.6537841 -45.0386040
784 785 786 787 788 789 790 791 792
-64.6847737 -33.1457799 -18.2541194 -24.1125119 -34.2248076 -11.8593712 -17.7574502 -21.1819371 50.4717005
793 794 795 796 797 798 799 800 801
-7.5742738 -3.1914229 -17.4634424 -68.7641172 -60.9842237 -105.5324665 -70.9347789 -24.4805555 -160.3057846
802 803 804 805 806 807 808 809 810
-42.3086889 13.2194255 -51.1477818 -64.5833293 -31.5386819 -70.9040251 -10.6832730 -14.1419752 109.8777505
811 812 813 814 815 816 817 818 819
-15.1581810 39.7165062 22.6622640 2.0622473 35.2852354 21.3097174 -6.7336675 -24.5980890 -31.6119752
820 821 822 823 824 825 826 827 828
-87.0736072 -75.1634528 -112.9723845 4.0613325 3.3618709 64.1808852 66.0214236 87.4027664 -10.9143311
829 830 831 832 833 834 835 836 837
2.1298817 -71.3214860 -36.4949629 -46.6884691 -37.6163574 -25.7612705 49.0695309 -9.6763574 11.7037295
838 839 840 841 842 843 844 845 846
104.1638284 33.9324352 26.3124352 8.1032377 -43.1604565 -43.8011760 -62.9624525 -82.0348602 -66.0691024
847 848 849 850 851 852 853 854 855
-14.7597509 99.6516729 63.6397630 18.6790318 1.9659440 45.8179055 52.3892359 -69.6488473 -99.4330063
856 857 858 859 860 861 862 863 864
-91.0250623 48.0789815 3.9383227 22.8120678 72.5335780 -13.4124799 92.9410900 15.5243449 14.2794148
865 866 867 868 869 870 871 872 873
0.8354764 11.2524435 7.2624876 -25.3199378 -31.3289386 -24.6681086 -22.5212810 29.1607689 11.6304764
874 875 876 877 878 879 880 881 882
17.6024435 4.7224876 -24.6426044 20.0358914 10.4987190 18.1487566 14.2794148 -3.6095236 4.9024435
883 884 885 886 887 888 889 890 891
27.5824876 -7.2859378 1.6910614 -23.1441086 -49.1912810 4.1194148 26.4471431 -16.6492434 -8.5512810
892 893 894 895 896 897 898 899 900
-19.5231494 -71.0080303 -81.1535613 -53.3898161 4.4274782 -58.8912083 -38.9964828 15.5425450 -28.3745693
901 902 903 904 905 906 907 908 909
-12.9863706 -78.4898475 -32.3730941 -39.3625490 -60.5619518 -36.0550029 -41.6664598 -66.5322412 -20.0174550
910 911 912 913 914 915 916 917 918
-40.8491369 -62.0295693 -26.5330372 -21.3398475 -50.4455746 -48.2180513 -17.5263846 -35.0622145 -23.3908816
919 920 921 922 923 924 925 926 927
-9.2582829 -3.6108934 -3.3273897 -11.8350022 -11.5712431 -10.3306073 -67.1900161 -32.5488691 -7.2489950
928 929 930 931 932 933 934 935 936
32.0924284 27.3075193 14.2250305 78.4532760 -7.5350802 -53.1130358 -24.6884473 8.4692631 -29.8094607
937 938 939 940 941 942 943 944 945
5.5755342 19.4465165 -5.5555230 -41.4785426 -54.7472143 -68.7388212 -73.7392677 -18.7156308 27.2586744
946 947 948 949 950 951 952 953 954
-32.8605230 -19.0667779 -1.5898181 -14.3018944 -19.5587588 20.4561527 -17.1956232 20.2315886 -40.2324877
955 956 957 958 959 960 961 962 963
-29.6247091 0.5289436 12.4879195 14.2334937 -39.0796751 -48.6658693 -86.5202849 -26.8499998 -9.7752300
964 965 966 967 968 969 970 971 972
-69.5543457 -56.1032705 -23.8708928 33.6457844 31.9238745 0.9331433 15.7866487 -44.7179035 -23.2454849
973 974 975 976 977 978 979 980 981
-98.2542458 -91.4288201 -111.4663078 -107.3331843 -120.5738563 -97.8426714 -48.6328025 286.7019697 112.7119697
982 983 984 985 986 987 988 989 990
111.4419697 -49.6892163 -36.9910757 -36.9910757 34.1187074 16.8684188 8.5459303 60.1580473 -18.1530478
991 992 993 994 995 996 997 998 999
21.3794838 -18.0077266 -29.0306928 45.0696246 67.9704691 41.6053813 44.6590695 47.1693072 -65.2952136
1000
-7.8501227
[ reached 'max' / getOption("max.print") -- omitted 3102 entries ]plot(sanvit.lm)
Post-hoc comparisons
sanvit.emm <- emmeans(sanvit.lm, ~ begin_date_year*age_group)
pairs(sanvit.emm, simple = "age_group")begin_date_year = 1992:
contrast estimate SE df t.ratio p.value
age_group0 - age_group1 -69.69 6.05 4073 -11.516 <.0001
age_group0 - age_group2 -157.28 5.92 4073 -26.585 <.0001
age_group0 - age_group3 -217.54 5.86 4073 -37.098 <.0001
age_group0 - age_group4 -268.04 5.91 4073 -45.357 <.0001
age_group0 - age_group5 -307.90 6.01 4073 -51.272 <.0001
age_group0 - age_group6 -345.39 6.06 4073 -57.038 <.0001
age_group0 - age_group7 -372.75 6.16 4073 -60.477 <.0001
age_group0 - age_group8 -396.84 6.34 4073 -62.566 <.0001
age_group0 - age_group9 -412.03 6.73 4073 -61.237 <.0001
age_group0 - age_group10 -432.90 6.93 4073 -62.442 <.0001
age_group0 - age_group11 -435.93 8.15 4073 -53.463 <.0001
age_group0 - age_group12 -459.93 9.43 4073 -48.749 <.0001
age_group1 - age_group2 -87.59 3.97 4073 -22.068 <.0001
age_group1 - age_group3 -147.85 3.86 4073 -38.305 <.0001
age_group1 - age_group4 -198.35 3.92 4073 -50.655 <.0001
age_group1 - age_group5 -238.21 4.03 4073 -59.072 <.0001
age_group1 - age_group6 -275.70 4.10 4073 -67.221 <.0001
age_group1 - age_group7 -303.06 4.26 4073 -71.155 <.0001
age_group1 - age_group8 -327.15 4.50 4073 -72.766 <.0001
age_group1 - age_group9 -342.34 5.01 4073 -68.378 <.0001
age_group1 - age_group10 -363.21 5.29 4073 -68.617 <.0001
age_group1 - age_group11 -366.25 6.81 4073 -53.763 <.0001
age_group1 - age_group12 -390.25 8.30 4073 -47.011 <.0001
age_group2 - age_group3 -60.26 3.56 4073 -16.914 <.0001
age_group2 - age_group4 -110.76 3.62 4073 -30.596 <.0001
age_group2 - age_group5 -150.62 3.74 4073 -40.278 <.0001
age_group2 - age_group6 -188.11 3.81 4073 -49.334 <.0001
age_group2 - age_group7 -215.47 3.98 4073 -54.119 <.0001
age_group2 - age_group8 -239.56 4.23 4073 -56.632 <.0001
age_group2 - age_group9 -254.75 4.76 4073 -53.470 <.0001
age_group2 - age_group10 -275.62 5.07 4073 -54.382 <.0001
age_group2 - age_group11 -278.65 6.64 4073 -41.977 <.0001
age_group2 - age_group12 -302.65 8.16 4073 -37.092 <.0001
age_group3 - age_group4 -50.50 3.49 4073 -14.487 <.0001
age_group3 - age_group5 -90.36 3.61 4073 -25.061 <.0001
age_group3 - age_group6 -127.85 3.68 4073 -34.727 <.0001
age_group3 - age_group7 -155.21 3.86 4073 -40.259 <.0001
age_group3 - age_group8 -179.30 4.11 4073 -43.633 <.0001
age_group3 - age_group9 -194.49 4.65 4073 -41.788 <.0001
age_group3 - age_group10 -215.36 4.97 4073 -43.364 <.0001
age_group3 - age_group11 -218.40 6.56 4073 -33.285 <.0001
age_group3 - age_group12 -242.40 8.10 4073 -29.938 <.0001
age_group4 - age_group5 -39.86 3.66 4073 -10.892 <.0001
age_group4 - age_group6 -77.35 3.73 4073 -20.719 <.0001
age_group4 - age_group7 -104.71 3.91 4073 -26.814 <.0001
age_group4 - age_group8 -128.79 4.15 4073 -30.998 <.0001
age_group4 - age_group9 -143.98 4.69 4073 -30.670 <.0001
age_group4 - age_group10 -164.86 5.01 4073 -32.933 <.0001
age_group4 - age_group11 -167.89 6.59 4073 -25.480 <.0001
age_group4 - age_group12 -191.89 8.12 4073 -23.631 <.0001
age_group5 - age_group6 -37.49 3.84 4073 -9.760 <.0001
age_group5 - age_group7 -64.85 4.01 4073 -16.184 <.0001
age_group5 - age_group8 -88.94 4.25 4073 -20.929 <.0001
age_group5 - age_group9 -104.13 4.77 4073 -21.807 <.0001
age_group5 - age_group10 -125.00 5.08 4073 -24.596 <.0001
age_group5 - age_group11 -128.03 6.65 4073 -19.256 <.0001
age_group5 - age_group12 -152.03 8.17 4073 -18.613 <.0001
age_group6 - age_group7 -27.36 4.07 4073 -6.715 <.0001
age_group6 - age_group8 -51.45 4.31 4073 -11.932 <.0001
age_group6 - age_group9 -66.64 4.83 4073 -13.793 <.0001
age_group6 - age_group10 -87.51 5.14 4073 -17.039 <.0001
age_group6 - age_group11 -90.55 6.69 4073 -13.536 <.0001
age_group6 - age_group12 -114.55 8.20 4073 -13.969 <.0001
age_group7 - age_group8 -24.09 4.46 4073 -5.398 <.0001
age_group7 - age_group9 -39.28 4.96 4073 -7.911 <.0001
age_group7 - age_group10 -60.15 5.26 4073 -11.433 <.0001
age_group7 - age_group11 -63.18 6.79 4073 -9.310 <.0001
age_group7 - age_group12 -87.18 8.28 4073 -10.529 <.0001
age_group8 - age_group9 -15.19 5.16 4073 -2.945 0.1424
age_group8 - age_group10 -36.06 5.45 4073 -6.623 <.0001
age_group8 - age_group11 -39.10 6.93 4073 -5.641 <.0001
age_group8 - age_group12 -63.10 8.40 4073 -7.514 <.0001
age_group9 - age_group10 -20.87 5.86 4073 -3.562 0.0223
age_group9 - age_group11 -23.91 7.26 4073 -3.291 0.0537
age_group9 - age_group12 -47.91 8.67 4073 -5.523 <.0001
age_group10 - age_group11 -3.03 7.47 4073 -0.406 1.0000
age_group10 - age_group12 -27.03 8.85 4073 -3.055 0.1064
age_group11 - age_group12 -24.00 9.83 4073 -2.441 0.4153
P value adjustment: tukey method for comparing a family of 13 estimates test(pairs(sanvit.emm, by = "begin_date_year"), by = NULL, adjust = "mvt") contrast begin_date_year estimate SE df t.ratio p.value
age_group0 - age_group1 1992 -69.69 6.05 4073 -11.516 <.0001
age_group0 - age_group2 1992 -157.28 5.92 4073 -26.585 <.0001
age_group0 - age_group3 1992 -217.54 5.86 4073 -37.098 <.0001
age_group0 - age_group4 1992 -268.04 5.91 4073 -45.357 <.0001
age_group0 - age_group5 1992 -307.90 6.01 4073 -51.272 <.0001
age_group0 - age_group6 1992 -345.39 6.06 4073 -57.038 <.0001
age_group0 - age_group7 1992 -372.75 6.16 4073 -60.477 <.0001
age_group0 - age_group8 1992 -396.84 6.34 4073 -62.566 <.0001
age_group0 - age_group9 1992 -412.03 6.73 4073 -61.237 <.0001
age_group0 - age_group10 1992 -432.90 6.93 4073 -62.442 <.0001
age_group0 - age_group11 1992 -435.93 8.15 4073 -53.463 <.0001
age_group0 - age_group12 1992 -459.93 9.43 4073 -48.749 <.0001
age_group1 - age_group2 1992 -87.59 3.97 4073 -22.068 <.0001
age_group1 - age_group3 1992 -147.85 3.86 4073 -38.305 <.0001
age_group1 - age_group4 1992 -198.35 3.92 4073 -50.655 <.0001
age_group1 - age_group5 1992 -238.21 4.03 4073 -59.072 <.0001
age_group1 - age_group6 1992 -275.70 4.10 4073 -67.221 <.0001
age_group1 - age_group7 1992 -303.06 4.26 4073 -71.155 <.0001
age_group1 - age_group8 1992 -327.15 4.50 4073 -72.766 <.0001
age_group1 - age_group9 1992 -342.34 5.01 4073 -68.378 <.0001
age_group1 - age_group10 1992 -363.21 5.29 4073 -68.617 <.0001
age_group1 - age_group11 1992 -366.25 6.81 4073 -53.763 <.0001
age_group1 - age_group12 1992 -390.25 8.30 4073 -47.011 <.0001
age_group2 - age_group3 1992 -60.26 3.56 4073 -16.914 <.0001
age_group2 - age_group4 1992 -110.76 3.62 4073 -30.596 <.0001
age_group2 - age_group5 1992 -150.62 3.74 4073 -40.278 <.0001
age_group2 - age_group6 1992 -188.11 3.81 4073 -49.334 <.0001
age_group2 - age_group7 1992 -215.47 3.98 4073 -54.119 <.0001
age_group2 - age_group8 1992 -239.56 4.23 4073 -56.632 <.0001
age_group2 - age_group9 1992 -254.75 4.76 4073 -53.470 <.0001
age_group2 - age_group10 1992 -275.62 5.07 4073 -54.382 <.0001
age_group2 - age_group11 1992 -278.65 6.64 4073 -41.977 <.0001
age_group2 - age_group12 1992 -302.65 8.16 4073 -37.092 <.0001
age_group3 - age_group4 1992 -50.50 3.49 4073 -14.487 <.0001
age_group3 - age_group5 1992 -90.36 3.61 4073 -25.061 <.0001
age_group3 - age_group6 1992 -127.85 3.68 4073 -34.727 <.0001
age_group3 - age_group7 1992 -155.21 3.86 4073 -40.259 <.0001
age_group3 - age_group8 1992 -179.30 4.11 4073 -43.633 <.0001
age_group3 - age_group9 1992 -194.49 4.65 4073 -41.788 <.0001
age_group3 - age_group10 1992 -215.36 4.97 4073 -43.364 <.0001
age_group3 - age_group11 1992 -218.40 6.56 4073 -33.285 <.0001
age_group3 - age_group12 1992 -242.40 8.10 4073 -29.938 <.0001
age_group4 - age_group5 1992 -39.86 3.66 4073 -10.892 <.0001
age_group4 - age_group6 1992 -77.35 3.73 4073 -20.719 <.0001
age_group4 - age_group7 1992 -104.71 3.91 4073 -26.814 <.0001
age_group4 - age_group8 1992 -128.79 4.15 4073 -30.998 <.0001
age_group4 - age_group9 1992 -143.98 4.69 4073 -30.670 <.0001
age_group4 - age_group10 1992 -164.86 5.01 4073 -32.933 <.0001
age_group4 - age_group11 1992 -167.89 6.59 4073 -25.480 <.0001
age_group4 - age_group12 1992 -191.89 8.12 4073 -23.631 <.0001
age_group5 - age_group6 1992 -37.49 3.84 4073 -9.760 <.0001
age_group5 - age_group7 1992 -64.85 4.01 4073 -16.184 <.0001
age_group5 - age_group8 1992 -88.94 4.25 4073 -20.929 <.0001
age_group5 - age_group9 1992 -104.13 4.77 4073 -21.807 <.0001
age_group5 - age_group10 1992 -125.00 5.08 4073 -24.596 <.0001
age_group5 - age_group11 1992 -128.03 6.65 4073 -19.256 <.0001
age_group5 - age_group12 1992 -152.03 8.17 4073 -18.613 <.0001
age_group6 - age_group7 1992 -27.36 4.07 4073 -6.715 <.0001
age_group6 - age_group8 1992 -51.45 4.31 4073 -11.932 <.0001
age_group6 - age_group9 1992 -66.64 4.83 4073 -13.793 <.0001
age_group6 - age_group10 1992 -87.51 5.14 4073 -17.039 <.0001
age_group6 - age_group11 1992 -90.55 6.69 4073 -13.536 <.0001
age_group6 - age_group12 1992 -114.55 8.20 4073 -13.969 <.0001
age_group7 - age_group8 1992 -24.09 4.46 4073 -5.398 <.0001
age_group7 - age_group9 1992 -39.28 4.96 4073 -7.911 <.0001
age_group7 - age_group10 1992 -60.15 5.26 4073 -11.433 <.0001
age_group7 - age_group11 1992 -63.18 6.79 4073 -9.310 <.0001
age_group7 - age_group12 1992 -87.18 8.28 4073 -10.529 <.0001
age_group8 - age_group9 1992 -15.19 5.16 4073 -2.945 0.1265
age_group8 - age_group10 1992 -36.06 5.45 4073 -6.623 <.0001
age_group8 - age_group11 1992 -39.10 6.93 4073 -5.641 <.0001
age_group8 - age_group12 1992 -63.10 8.40 4073 -7.514 <.0001
age_group9 - age_group10 1992 -20.87 5.86 4073 -3.562 0.0191
age_group9 - age_group11 1992 -23.91 7.26 4073 -3.291 0.0473
age_group9 - age_group12 1992 -47.91 8.67 4073 -5.523 <.0001
age_group10 - age_group11 1992 -3.03 7.47 4073 -0.406 1.0000
age_group10 - age_group12 1992 -27.03 8.85 4073 -3.055 0.0950
age_group11 - age_group12 1992 -24.00 9.83 4073 -2.441 0.3871
P value adjustment: mvt method for 78 tests #export tables
# #interpret(eta_squared(sanvit.lm), rules = "cohen1992") %>%
# write.csv(file = "Outputs/Tables/sanvit_eta_squared.csv")
#slope contrasts
# Obtain slope estimates
sanvit.slopes <- emtrends(sanvit.lm, ~age_group, var = "begin_date_year")
# Compare estimated slopes to 0
sanvit.slope.contrasts <- test(sanvit.slopes) %>%
mutate(Species = "Walleye") %>%
rename(Age = age_group)
sanvit.slope.contrasts %>%
write.csv(file = "Outputs/Tables/sanvit_emmeans.csv")Plot raw data
(sanvit.length.year.plot <- ggplot(data = sanvit %>%
mutate(Age = as.factor(age_group)),
aes(x = begin_date_year, y = length_mean_mm,
color = Age, fill = Age, group = Age))+
#geom_point()+
geom_smooth(method = "lm")+
stat_poly_eq(use_label("eq"))+
scale_color_viridis_d()+
scale_fill_viridis_d()+
labs(y = "Length (mm)", x = "Year")+
theme_bw()
)`geom_smooth()` using formula = 'y ~ x'
Marginal Effects
result <- predict_response(sanvit.lm, c("begin_date_year", "age_group"))
# plot(result)
# test_predictions(result) %>%
# write.csv(file = "Outputs/Tables/sanvit_pairwise_length_time_slopes.csv", row.names = F)
(sanvit.marginal.plot <- ggpredict(sanvit.lm, terms = c("begin_date_year", "age_group")) %>%
plot()+
#stat_poly_eq(use_label("eq"))+
stat_regline_equation()+
stat_poly_line()+
# annotate(geom = "text", label = "y = 77 + 0.07x", x = 2000, y = 224)+
# annotate(geom = "text", label = "y = 21 + 0.093x", x = 2000, y = 212)+
# annotate(geom = "text", label = "y = -64 + 0.13x", x = 2000, y = 199)+
# annotate(geom = "text", label = "y = 230 - 0.028x", x = 2000, y = 182)+
# annotate(geom = "text", label = "y = 610 - 0.23x", x = 2000, y = 160)+
# annotate(geom = "text", label = "y = 920 - 0.39x", x = 2000, y = 137)+
# annotate(geom = "text", label = "y = 1200 - 0.55x", x = 2000, y = 110)+
# annotate(geom = "text", label = "y = 1700 - 0.83x", x = 2000, y = 80)+
scale_color_viridis_d()+
scale_fill_viridis_d()+
labs(title = NULL,
y = "Mean Length-at-Age (mm)",
x = "Year")+
guides(fill = guide_legend(title = "Age", reverse = T), color = guide_legend(title = "Age", reverse = T))+
theme_bw()
)Scale for colour is already present.
Adding another scale for colour, which will replace the existing scale.ggsave(filename = "~/GitHub/EAGER_growth/Outputs/Figures/Linear Models/sanvit_marginal_effects_plot.tiff",
sanvit.marginal.plot,
dpi = 300, width = 200, height = 150, units = "mm")
Yellow Perch
Model
#filter data for only Black Crappie, Pomooxis nigromaculatus
perfla <- all.grow.merge %>% filter(species == "yellow_perch") %>%
filter(age_group %in% c(0:11), #match age groups in BRTs
!is.na(age_group), !is.na(begin_date_year), !is.na(length_mean_mm),
!is.na(log_max_depth), !is.na(logarea))
#linear model
perfla.lm <- lm(length_mean_mm ~ begin_date_year*age_group + log_max_depth + logarea + doy, data = perfla)
summary(perfla.lm)
Call:
lm(formula = length_mean_mm ~ begin_date_year * age_group + log_max_depth +
logarea + doy, data = perfla)
Residuals:
Min 1Q Median 3Q Max
-133.799 -18.629 -1.851 17.320 257.558
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 1.094e+03 2.850e+02 3.840 0.000124 ***
begin_date_year -5.300e-01 1.436e-01 -3.690 0.000226 ***
age_group1 -1.097e+02 3.069e+02 -0.357 0.720818
age_group2 -2.079e+02 2.980e+02 -0.698 0.485462
age_group3 -6.049e+02 2.962e+02 -2.042 0.041140 *
age_group4 -7.420e+02 2.977e+02 -2.493 0.012698 *
age_group5 -5.713e+02 3.012e+02 -1.897 0.057890 .
age_group6 -3.893e+02 3.082e+02 -1.263 0.206609
age_group7 -5.180e+00 3.257e+02 -0.016 0.987314
age_group8 -6.295e+02 3.562e+02 -1.767 0.077239 .
age_group9 -6.536e+02 4.080e+02 -1.602 0.109177
age_group10 -2.473e+02 5.075e+02 -0.487 0.626005
age_group11 -6.450e+02 6.696e+02 -0.963 0.335409
log_max_depth 2.929e+00 4.529e-01 6.467 1.05e-10 ***
logarea -9.262e-01 2.117e-01 -4.375 1.22e-05 ***
doy 1.424e-01 5.469e-03 26.030 < 2e-16 ***
begin_date_year:age_group1 7.240e-02 1.546e-01 0.468 0.639653
begin_date_year:age_group2 1.409e-01 1.502e-01 0.938 0.348353
begin_date_year:age_group3 3.549e-01 1.493e-01 2.378 0.017437 *
begin_date_year:age_group4 4.367e-01 1.500e-01 2.911 0.003607 **
begin_date_year:age_group5 3.637e-01 1.518e-01 2.396 0.016582 *
begin_date_year:age_group6 2.836e-01 1.553e-01 1.826 0.067824 .
begin_date_year:age_group7 1.011e-01 1.641e-01 0.616 0.537959
begin_date_year:age_group8 4.247e-01 1.794e-01 2.368 0.017921 *
begin_date_year:age_group9 4.451e-01 2.053e-01 2.168 0.030198 *
begin_date_year:age_group10 2.468e-01 2.552e-01 0.967 0.333493
begin_date_year:age_group11 4.544e-01 3.365e-01 1.350 0.176993
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 30.03 on 9719 degrees of freedom
(128 observations deleted due to missingness)
Multiple R-squared: 0.7595, Adjusted R-squared: 0.7589
F-statistic: 1181 on 26 and 9719 DF, p-value: < 2.2e-16#calculate and interpret effect sizes
eta_squared(perfla.lm) begin_date_year age_group log_max_depth logarea
0.0046698302 0.7346345820 0.0010433333 0.0008060225
doy begin_date_year:age_group
0.0170544674 0.0013303507 #interpret(eta_squared(perfla.lm), rules = "cohen1992")
#calculate AIC score
AIC(perfla.lm)[1] 94005.44#examine model fit
testDispersion(perfla.lm)
DHARMa nonparametric dispersion test via sd of residuals fitted vs. simulated
data: simulationOutput
dispersion = 0.99729, p-value = 0.816
alternative hypothesis: two.sided
simulation.output <- simulateResiduals(fittedModel = perfla.lm)
residuals(perfla.lm) 1 2 3 4 5 6 7 8 9 10
3.5062776 6.5769668 -2.6388778 -30.7001491 -16.7140331 -41.2079578 -47.9331207 -40.9315647 -29.9907342 -42.2908646
11 12 13 14 15 16 17 18 19 20
-55.8378501 23.9071761 34.2811652 18.2303404 18.5967900 1.1188089 -0.4540159 -33.2563714 21.2964526 34.7096278
21 22 23 24 25 26 27 28 29 30
-4.4823065 -15.0633174 -6.7301422 14.3373073 42.6365023 30.2012389 6.2478365 -3.0284430 25.4476362 3.6483607
31 32 33 34 35 36 37 38 39 40
14.3140072 -5.9237625 -4.8318762 -2.9126923 2.1673077 -6.0661188 9.6449513 46.0441025 -33.2786548 33.5581912
41 42 43 44 45 46 47 48 49 50
-7.8760731 -24.0176201 -14.9922284 -18.0752912 -10.1367734 1.2951799 -17.3267920 -18.2251187 -53.3223849 -16.2116216
51 52 53 54 55 56 57 58 59 60
-20.8222358 -46.2222857 18.5162773 -47.7909592 -7.5077829 -6.0018523 2.4382280 17.3842171 17.4478419 12.6572574
61 62 63 64 65 66 67 68 69 70
-21.6318190 -3.3591635 24.8508760 -44.1734992 -22.4676378 -13.4631131 -3.0891240 2.7040512 44.7265008 -18.0341230
71 72 73 74 75 76 77 78 79 80
-9.8972639 -35.1752160 -24.5757667 6.3869753 14.0069753 9.7142333 35.3294113 48.5024277 20.8471571 31.1695436
81 82 83 84 85 86 87 88 89 90
-20.0775723 -4.7844765 -32.4728127 -12.9019907 -55.7799286 -32.2264793 -19.2977373 -18.2953013 15.6873782 6.4289864
91 92 93 94 95 96 97 98 99 100
-0.8728356 -17.5420519 -21.0218038 -18.7427415 -9.9623829 39.4041590 32.0675137 58.5376586 74.1831595 106.3884130
101 102 103 104 105 106 107 108 109 110
31.9251574 16.9086356 66.9149408 38.5561344 62.0629460 23.7334469 80.7037003 23.8706172 17.5145964 -9.7429410
111 112 113 114 115 116 117 118 119 120
-23.5299440 -12.0952492 -26.1169093 4.8344172 6.6701633 -17.8847204 -13.8452213 -19.1547204 14.9555330 14.9944172
121 122 123 124 125 126 127 128 129 130
41.6255330 -23.3762209 -16.2105765 -20.3003538 10.8758100 -11.3491900 106.9486941 4.7333214 70.5581434 32.6733214
131 132 133 134 135 136 137 138 139 140
20.1929969 1.5310639 -17.6580980 -25.3117465 7.4929969 11.9450639 -9.6147647 -17.6917465 20.1929969 -22.9059029
141 142 143 144 145 146 147 148 149 150
-14.5861015 -22.9059029 -16.3744744 -29.8261015 -19.0525017 -52.1776408 -50.0647009 -2.2745122 -13.3232798 -18.1244864
151 152 153 154 155 156 157 158 159 160
-25.9216311 -29.6370050 27.2564015 6.3311041 48.8521292 75.0471358 38.2565364 46.7606527 18.6136808 -20.5963126
161 162 163 164 165 166 167 168 169 170
-3.2002454 17.6548959 -18.1986689 -2.3749826 8.7287478 26.1352931 -34.9298460 -4.8769061 109.3130658 53.1629544
171 172 173 174 175 176 177 178 179 180
15.4420194 50.6229544 -9.3328999 5.1724362 68.9890720 9.4180339 9.1351268 -11.6451577 -29.8771607 -10.0865213
181 182 183 184 185 186 187 188 189 190
-19.7171607 -29.4491326 -11.7801440 20.5083022 -19.9095111 12.3912326 25.8104889 46.0048560 -22.7847048 -22.6909155
191 192 193 194 195 196 197 198 199 200
-43.0645420 10.1659777 -13.5501355 -10.7375636 2.5459777 -18.6301355 0.5804273 1.2902612 -9.5190223 -10.6486671
201 202 203 204 205 206 207 208 209 210
4.5801257 9.3098645 -0.7611433 20.2720319 8.2674774 -2.2376807 8.6697688 -7.4810362 -8.9096329 8.3270607
211 212 213 214 215 216 217 218 219 220
-16.7302312 -13.8344845 -15.5129009 -27.0526373 0.4152046 -19.0303459 19.9849208 41.8840762 -28.8966178 39.7885213
221 222 223 224 225 226 227 228 229 230
37.7078266 34.1763220 10.3596273 70.2448215 81.5548885 106.3077266 57.2545358 82.5708885 79.6377266 88.0707448
231 232 233 234 235 236 237 238 239 240
31.1288215 73.2575552 56.7440781 35.3531176 66.6888215 70.7477266 107.5440781 38.2433117 20.0586660 12.8433117
241 242 243 244 245 246 247 248 249 250
14.7928174 -4.4571835 6.0886660 0.6228165 10.7828165 -1.9171835 -2.8013340 16.8122223 34.1172290 34.1566295
251 252 253 254 255 256 257 258 259 260
22.6872290 20.1472290 38.2457467 1.4979355 -45.3576457 -26.1145324 -9.4047769 -12.7385903 -29.5868466 -40.8121854
261 262 263 264 265 266 267 268 269 270
-24.2121614 -14.2679745 -24.4279745 -12.3516343 -5.4607870 -17.8377068 -9.6113078 -17.6002798 -27.3772270 5.3766121
271 272 273 274 275 276 277 278 279 280
-42.6172270 -3.1958879 -32.9249079 -42.6172270 44.2371141 29.0112979 43.3416302 26.0708346 27.2996411 7.1783049
281 282 283 284 285 286 287 288 289 290
21.9967578 27.1756082 57.9312739 8.7834783 16.9972848 4.1572819 -0.1757656 45.6382635 65.5719841 -8.1589119
291 292 293 294 295 296 297 298 299 300
-5.8090177 10.1445049 34.8309823 8.7986501 -8.4631337 -5.9654694 -8.1589119 -15.4817702 -16.7741902 46.5117211
301 302 303 304 305 306 307 308 309 310
40.9697810 29.2334866 17.2381561 9.5114586 25.0271303 27.8000855 6.8291023 -17.1819359 2.0247740 41.0834435
311 312 313 314 315 316 317 318 319 320
60.0267459 2.7128007 1.8646050 69.4435098 48.8121793 0.8151059 -14.2666977 24.7266175 20.2098704 -12.8450489
321 322 323 324 325 326 327 328 329 330
-9.2349041 -29.9053555 -8.2445843 -16.6539766 -18.3521043 -22.5199234 -8.3808987 7.6714816 4.3278610 -16.4891241
331 332 333 334 335 336 337 338 339 340
15.0811294 8.7700135 -21.6282286 -39.3274543 -55.2126342 56.8242498 50.0276088 -1.2725003 35.3673939 21.4759165
341 342 343 344 345 346 347 348 349 350
20.8176088 4.8234997 6.1573939 18.2250616 -1.0749960 -9.1967222 19.5709165 -0.7723912 65.2909165 -35.7250592
351 352 353 354 355 356 357 358 359 360
-75.2297489 -13.3255935 6.6020309 1.3796746 33.9105050 -32.1809667 -6.7667829 8.3255494 1.7227538 6.2954538
361 362 363 364 365 366 367 368 369 370
-13.1121767 -18.4779148 -26.1228502 -40.7330177 -40.2995927 11.1992519 -53.4330177 12.7206927 15.9438283 0.8744662
371 372 373 374 375 376 377 378 379 380
14.6274350 12.1849237 17.9903191 17.5831017 9.7050104 -1.6750536 -21.0323741 10.4915904 14.1275816 -2.6599213
381 382 383 384 385 386 387 388 389 390
56.3874121 -20.7412754 0.8660832 -6.9490274 -4.0515302 3.1081973 -26.8574120 -6.2705227 -25.5500218 18.7202317
391 392 393 394 395 396 397 398 399 400
26.6071210 -9.3621246 18.6562058 15.2346198 24.5500433 -14.5917340 -23.5475702 5.3365852 -11.1855493 -17.6451907
401 402 403 404 405 406 407 408 409 410
-27.0326823 -31.0294709 4.3147072 6.0070527 -31.2415860 -21.5565793 7.7822483 28.1001003 -14.2310850 -17.9680129
411 412 413 414 415 416 417 418 419 420
23.6684555 -12.9228347 16.4666695 23.5281003 16.0484555 4.8919871 -5.9640830 -4.0229752 -22.2311029 -10.7567553
421 422 423 424 425 426 427 428 429 430
-32.3911029 -4.8300887 6.0848413 14.7276026 -3.6044363 -6.9467553 -27.7818254 2.7899113 45.8781746 23.7666282
431 432 433 434 435 436 437 438 439 440
26.9154602 0.6847328 36.2586676 22.9199616 11.6754602 5.7786178 16.5699616 16.1204602 10.0869010 28.6386676
441 442 443 444 445 446 447 448 449 450
17.2086178 7.6799616 12.8899924 -29.9575666 -25.3056943 -42.3005133 -53.7164167 -34.0669887 -0.4395571 16.0667866
451 452 453 454 455 456 457 458 459 460
12.4422852 24.7588424 -5.7312238 48.0022852 3.4974429 17.6542866 37.0115578 -23.7581999 8.5314403 -7.4245073
461 462 463 464 465 466 467 468 469 470
3.7514429 8.4467866 -13.0372635 12.2718442 20.3708593 13.5813974 15.6909941 -10.1737176 23.6830285 2.4997061
471 472 473 474 475 476 477 478 479 480
8.6195936 15.9550717 7.4298882 15.5050080 -15.2135547 -15.2537176 -0.0137176 -2.7329715 -18.8181510 -24.1155585
481 482 483 484 485 486 487 488 489 490
-4.3145785 -5.1168976 -7.4895785 -15.5943976 -9.8653009 -54.9858729 13.8935492 -12.1885785 -4.1008976 -25.1053009
491 492 493 494 495 496 497 498 499 500
-21.9658729 -46.5534926 -5.3195585 16.4731024 -24.5058729 20.6265487 43.0584210 23.8411019 4.4876986 -23.2762067
501 502 503 504 505 506 507 508 509 510
25.0138214 5.4261019 -17.7728734 35.8665487 31.6284210 -4.7338981 -34.2828734 11.3194410 38.9145487 15.1184210
511 512 513 514 515 516 517 518 519 520
-19.9458326 -0.6960771 9.6861096 24.8418533 19.3315144 30.2165385 3.7489229 -6.8238904 30.8108533 0.9165144
521 522 523 524 525 526 527 528 529 530
-15.7138904 10.2298477 -7.5421900 -3.0124992 -6.6227437 6.2994429 -54.5331467 75.9365385 35.2710911 32.1031581
531 532 533 534 535 536 537 538 539 540
30.4399961 -4.5186524 32.5887348 31.5374684 15.6349125 12.4839913 49.5130308 30.5484454 -0.3247165 30.1216350
541 542 543 544 545 546 547 548 549 550
51.9106745 48.7110618 36.6929427 25.7822930 1.4455504 -26.9252928 -39.6160134 11.9689715 47.8643951 8.7529427
551 552 553 554 555 556 557 558 559 560
33.8943951 8.7529427 23.4213914 -10.8515376 -19.1289091 -24.1633100 -14.3681195 -16.2112777 -9.6763414 -18.9743099
561 562 563 564 565 566 567 568 569 570
-30.7156404 -27.0123380 -6.5069896 -29.3010480 -39.5574420 -38.9162725 6.0985544 -4.6829574 -6.9266738 -27.1233301
571 572 573 574 575 576 577 578 579 580
45.0919430 19.9959611 7.9550844 21.2332392 -15.6892847 -20.0368536 -2.3118514 8.5659611 -39.5764193 -58.2873423
581 582 583 584 585 586 587 588 589 590
-18.9000572 -46.2905449 -19.3619972 -14.9216474 1.4024515 -14.2409685 -39.2200572 -13.2705449 18.7380028 -34.0826469
591 592 593 594 595 596 597 598 599 600
-5.5402152 9.2540315 -8.4860572 -18.3505449 -4.1219972 -6.7080572 -23.4305449 12.3880028 -7.4126469 31.9949581
601 602 603 604 605 606 607 608 609 610
20.0024727 -3.3325206 -3.4342312 -0.4740029 11.1641661 24.5773413 3.5441661 -2.5159920 -16.7758339 -23.0476587
611 612 613 614 615 616 617 618 619 620
-40.9268758 -11.1744672 28.1296965 37.8425806 42.0920299 51.4647719 -18.3928951 -18.5807780 -1.9502618 5.8742987
621 622 623 624 625 626 627 628 629 630
4.2111367 -12.3325118 9.1186090 10.4187804 15.6765671 11.1020364 15.6765671 15.5047031 17.8401034 -2.3339254
631 632 633 634 635 636 637 638 639 640
24.1065756 48.4801623 -33.5191061 10.7228834 -19.3676776 -47.3036075 -6.2104499 -15.7391061 -11.7436075 -25.2604001
641 642 643 644 645 646 647 648 649 650
-23.5671166 -24.6319839 3.9691276 -23.8957754 -25.0264036 -15.7505541 -31.8559085 16.4040915 31.0535973 -15.4942370
651 652 653 654 655 656 657 658 659 660
7.3450218 -0.0919812 -32.6839531 -23.9504288 12.7265653 44.0551098 30.9079939 75.8709460 50.1003670 54.1385897
661 662 663 664 665 666 667 668 669 670
48.9927535 20.3035755 38.6703670 45.2485897 27.0769089 -15.0458489 -6.9689626 7.0152212 -8.7957798 -3.8371399
671 672 673 674 675 676 677 678 679 680
-35.1733455 -14.5493274 22.2479507 -14.9496399 -33.4800122 -19.6293274 -20.4216327 37.4879507 -52.7745732 -21.6171399
681 682 683 684 685 686 687 688 689 690
-36.5626608 16.6748393 -10.6325828 -1.6164038 5.8388105 -1.7910059 18.5873266 16.5800087 9.5095211 -20.9887244
691 692 693 694 695 696 697 698 699 700
17.3858278 24.9785419 18.1040087 1.8895211 39.9895211 -37.6028776 -21.3968957 -45.1077652 17.0742314 26.0298701
701 702 703 704 705 706 707 708 709 710
26.0065624 -4.9735467 -13.8234783 1.5104741 3.4390824 -3.8222283 4.1269153 8.9359937 0.6941222 24.9645485
711 712 713 714 715 716 717 718 719 720
36.8475028 -44.4324972 -23.8813731 -37.8727733 -33.5329124 -41.1989726 -36.6100007 -25.4181184 -24.3566771 -1.0747996
721 722 723 724 725 726 727 728 729 730
10.6810798 -19.3885468 74.0375450 53.7337634 56.0285847 29.0976379 8.8751266 35.4238554 5.8066380 39.7468075
731 732 733 734 735 736 737 738 739 740
33.0134712 8.8751266 12.5638554 10.5607325 5.6229595 1.1742887 2.5172781 -2.2416924 -45.7757036 12.5802042
741 742 743 744 745 746 747 748 749 750
11.3029942 -9.5665020 -15.0884257 -24.7118506 -45.8582127 -21.9995442 -22.4181828 -23.3165095 9.0443530 -18.7552918
751 752 753 754 755 756 757 758 759 760
0.1686364 0.1543530 -33.1486252 0.9185009 -13.8156470 -13.6752918 -27.0214991 27.9335555 7.1018303 -5.4292802
761 762 763 764 765 766 767 768 769 770
4.8431694 28.0051883 19.5743635 20.0288429 21.5128320 4.4460072 2.0184568 -12.7689740 5.1860269 -20.8466506
771 772 773 774 775 776 777 778 779 780
23.4029271 35.3950719 31.1650729 35.7478263 20.6935937 -1.9429281 46.9342395 43.3678263 7.1818464 11.2109271
781 782 783 784 785 786 787 788 789 790
19.3930719 29.5775729 41.8438263 29.4367104 -22.9012308 -26.1118028 -4.7569478 -15.4963016 -43.7993011 -36.5582869
791 792 793 794 795 796 797 798 799 800
-55.2766902 20.2334426 -4.7528785 1.0119168 -13.0284800 -39.8682786 -51.8714368 21.2064191 15.2199714 13.9499216
801 802 803 804 805 806 807 808 809 810
28.1279321 16.4760366 12.4524905 -29.3231362 12.5774856 46.5191952 0.3839324 3.5178577 -14.1861899 -6.6610094
811 812 813 814 815 816 817 818 819 820
-1.2464926 -18.4342621 -12.4277720 -8.6589656 -23.7528910 5.1691351 -13.4504552 -14.0926218 -6.0867723 18.4589094
821 822 823 824 825 826 827 828 829 830
-10.9104552 -10.2826218 72.6532277 121.9973791 -18.5304552 1.1473782 9.1532277 5.1239094 10.2562115 6.7353782
831 832 833 834 835 836 837 838 839 840
10.5949238 -22.4250762 4.2155105 -21.9976032 -10.9337132 -15.4560403 -23.9909401 -30.8574474 -43.6264264 -35.3008679
841 842 843 844 845 846 847 848 849 850
-29.8791615 -10.9959481 -7.1665760 7.4675304 28.8881335 12.6460722 11.6575386 -18.6220020 -26.0416952 -20.9222947
851 852 853 854 855 856 857 858 859 860
-23.1009887 -16.0240515 -40.1146510 -8.0033450 -57.6014145 -13.6264078 -5.6878901 -13.2257013 -18.1712825 -12.5826706
861 862 863 864 865 866 867 868 869 870
-18.0524509 -28.9782857 -41.5841798 -37.1823992 -41.7482078 -35.2792460 -62.2124049 -20.2066323 11.8633628 4.7936427
871 872 873 874 875 876 877 878 879 880
-2.6011082 -23.4975845 -20.2607427 6.1390738 2.7841937 -21.7165979 -11.1196009 -15.7715728 16.8128343 -21.7737137
881 882 883 884 885 886 887 888 889 890
-13.5772688 -4.6289191 3.0343111 -3.0709505 -5.6296382 -3.5318709 11.7497854 0.1168101 -27.1858675 21.4764239
891 892 893 894 895 896 897 898 899 900
10.7117312 -2.7950242 -5.4910674 -17.6502976 8.4033238 -10.6690962 27.2168151 16.5948750 -22.2557746 11.7004492
901 902 903 904 905 906 907 908 909 910
-6.0795508 -6.1436892 -57.7148650 14.0030355 3.4413679 -0.5097840 -4.3609776 -13.8989804 -2.3099031 3.1578597
911 912 913 914 915 916 917 918 919 920
3.5143217 -26.0854586 -23.7731796 -15.7202758 -31.9750714 -56.1462650 4.1657322 23.1051958 -40.6887934 -14.4777362
921 922 923 924 925 926 927 928 929 930
-16.6365521 -40.7257940 -28.3217170 -37.3598336 -9.1886985 -30.1775078 26.6909185 -20.7665021 -5.5559476 51.0591733
931 932 933 934 935 936 937 938 939 940
-11.8711140 -35.0399227 -39.3132669 -32.4194250 -44.5836421 -13.7444472 68.3986210 -2.0781637 0.4216735 25.8305933
941 942 943 944 945 946 947 948 949 950
1.8985376 19.6785376 7.6033088 5.0311912 -5.2944231 -9.2556634 -1.4881291 -23.3159639 5.9117696 32.6962943
951 952 953 954 955 956 957 958 959 960
37.9902834 33.6234586 15.0451031 10.2298397 45.9820021 48.7441888 53.1049325 47.9574507 37.2706177 26.5033841
961 962 963 964 965 966 967 968 969 970
48.3222466 33.2820021 7.0881888 47.5945936 48.9546177 0.5909013 23.5572466 35.8220021 -27.4558112 20.7314861
971 972 973 974 975 976 977 978 979 980
-24.6844958 15.6514861 29.2076917 2.6491566 -8.6546644 12.4432336 0.9558233 -55.3861167 -0.3141767 10.8186690
981 982 983 984 985 986 987 988 989 990
-0.3823102 1.3186804 -11.1946644 20.1929969 1.5310639 -17.6580980 -25.3117465 7.4929969 11.9450639 -9.6147647
991 992 993 994 995 996 997 998 999 1000
-17.6917465 20.1929969 18.6067592 24.2559230 14.4050784 0.8580928 7.0055641 14.5709984 -9.1915656 54.1667592
[ reached 'max' / getOption("max.print") -- omitted 8746 entries ]residuals(perfla.lm, quantileFunction = qnorm) 1 2 3 4 5 6 7 8 9 10
3.5062776 6.5769668 -2.6388778 -30.7001491 -16.7140331 -41.2079578 -47.9331207 -40.9315647 -29.9907342 -42.2908646
11 12 13 14 15 16 17 18 19 20
-55.8378501 23.9071761 34.2811652 18.2303404 18.5967900 1.1188089 -0.4540159 -33.2563714 21.2964526 34.7096278
21 22 23 24 25 26 27 28 29 30
-4.4823065 -15.0633174 -6.7301422 14.3373073 42.6365023 30.2012389 6.2478365 -3.0284430 25.4476362 3.6483607
31 32 33 34 35 36 37 38 39 40
14.3140072 -5.9237625 -4.8318762 -2.9126923 2.1673077 -6.0661188 9.6449513 46.0441025 -33.2786548 33.5581912
41 42 43 44 45 46 47 48 49 50
-7.8760731 -24.0176201 -14.9922284 -18.0752912 -10.1367734 1.2951799 -17.3267920 -18.2251187 -53.3223849 -16.2116216
51 52 53 54 55 56 57 58 59 60
-20.8222358 -46.2222857 18.5162773 -47.7909592 -7.5077829 -6.0018523 2.4382280 17.3842171 17.4478419 12.6572574
61 62 63 64 65 66 67 68 69 70
-21.6318190 -3.3591635 24.8508760 -44.1734992 -22.4676378 -13.4631131 -3.0891240 2.7040512 44.7265008 -18.0341230
71 72 73 74 75 76 77 78 79 80
-9.8972639 -35.1752160 -24.5757667 6.3869753 14.0069753 9.7142333 35.3294113 48.5024277 20.8471571 31.1695436
81 82 83 84 85 86 87 88 89 90
-20.0775723 -4.7844765 -32.4728127 -12.9019907 -55.7799286 -32.2264793 -19.2977373 -18.2953013 15.6873782 6.4289864
91 92 93 94 95 96 97 98 99 100
-0.8728356 -17.5420519 -21.0218038 -18.7427415 -9.9623829 39.4041590 32.0675137 58.5376586 74.1831595 106.3884130
101 102 103 104 105 106 107 108 109 110
31.9251574 16.9086356 66.9149408 38.5561344 62.0629460 23.7334469 80.7037003 23.8706172 17.5145964 -9.7429410
111 112 113 114 115 116 117 118 119 120
-23.5299440 -12.0952492 -26.1169093 4.8344172 6.6701633 -17.8847204 -13.8452213 -19.1547204 14.9555330 14.9944172
121 122 123 124 125 126 127 128 129 130
41.6255330 -23.3762209 -16.2105765 -20.3003538 10.8758100 -11.3491900 106.9486941 4.7333214 70.5581434 32.6733214
131 132 133 134 135 136 137 138 139 140
20.1929969 1.5310639 -17.6580980 -25.3117465 7.4929969 11.9450639 -9.6147647 -17.6917465 20.1929969 -22.9059029
141 142 143 144 145 146 147 148 149 150
-14.5861015 -22.9059029 -16.3744744 -29.8261015 -19.0525017 -52.1776408 -50.0647009 -2.2745122 -13.3232798 -18.1244864
151 152 153 154 155 156 157 158 159 160
-25.9216311 -29.6370050 27.2564015 6.3311041 48.8521292 75.0471358 38.2565364 46.7606527 18.6136808 -20.5963126
161 162 163 164 165 166 167 168 169 170
-3.2002454 17.6548959 -18.1986689 -2.3749826 8.7287478 26.1352931 -34.9298460 -4.8769061 109.3130658 53.1629544
171 172 173 174 175 176 177 178 179 180
15.4420194 50.6229544 -9.3328999 5.1724362 68.9890720 9.4180339 9.1351268 -11.6451577 -29.8771607 -10.0865213
181 182 183 184 185 186 187 188 189 190
-19.7171607 -29.4491326 -11.7801440 20.5083022 -19.9095111 12.3912326 25.8104889 46.0048560 -22.7847048 -22.6909155
191 192 193 194 195 196 197 198 199 200
-43.0645420 10.1659777 -13.5501355 -10.7375636 2.5459777 -18.6301355 0.5804273 1.2902612 -9.5190223 -10.6486671
201 202 203 204 205 206 207 208 209 210
4.5801257 9.3098645 -0.7611433 20.2720319 8.2674774 -2.2376807 8.6697688 -7.4810362 -8.9096329 8.3270607
211 212 213 214 215 216 217 218 219 220
-16.7302312 -13.8344845 -15.5129009 -27.0526373 0.4152046 -19.0303459 19.9849208 41.8840762 -28.8966178 39.7885213
221 222 223 224 225 226 227 228 229 230
37.7078266 34.1763220 10.3596273 70.2448215 81.5548885 106.3077266 57.2545358 82.5708885 79.6377266 88.0707448
231 232 233 234 235 236 237 238 239 240
31.1288215 73.2575552 56.7440781 35.3531176 66.6888215 70.7477266 107.5440781 38.2433117 20.0586660 12.8433117
241 242 243 244 245 246 247 248 249 250
14.7928174 -4.4571835 6.0886660 0.6228165 10.7828165 -1.9171835 -2.8013340 16.8122223 34.1172290 34.1566295
251 252 253 254 255 256 257 258 259 260
22.6872290 20.1472290 38.2457467 1.4979355 -45.3576457 -26.1145324 -9.4047769 -12.7385903 -29.5868466 -40.8121854
261 262 263 264 265 266 267 268 269 270
-24.2121614 -14.2679745 -24.4279745 -12.3516343 -5.4607870 -17.8377068 -9.6113078 -17.6002798 -27.3772270 5.3766121
271 272 273 274 275 276 277 278 279 280
-42.6172270 -3.1958879 -32.9249079 -42.6172270 44.2371141 29.0112979 43.3416302 26.0708346 27.2996411 7.1783049
281 282 283 284 285 286 287 288 289 290
21.9967578 27.1756082 57.9312739 8.7834783 16.9972848 4.1572819 -0.1757656 45.6382635 65.5719841 -8.1589119
291 292 293 294 295 296 297 298 299 300
-5.8090177 10.1445049 34.8309823 8.7986501 -8.4631337 -5.9654694 -8.1589119 -15.4817702 -16.7741902 46.5117211
301 302 303 304 305 306 307 308 309 310
40.9697810 29.2334866 17.2381561 9.5114586 25.0271303 27.8000855 6.8291023 -17.1819359 2.0247740 41.0834435
311 312 313 314 315 316 317 318 319 320
60.0267459 2.7128007 1.8646050 69.4435098 48.8121793 0.8151059 -14.2666977 24.7266175 20.2098704 -12.8450489
321 322 323 324 325 326 327 328 329 330
-9.2349041 -29.9053555 -8.2445843 -16.6539766 -18.3521043 -22.5199234 -8.3808987 7.6714816 4.3278610 -16.4891241
331 332 333 334 335 336 337 338 339 340
15.0811294 8.7700135 -21.6282286 -39.3274543 -55.2126342 56.8242498 50.0276088 -1.2725003 35.3673939 21.4759165
341 342 343 344 345 346 347 348 349 350
20.8176088 4.8234997 6.1573939 18.2250616 -1.0749960 -9.1967222 19.5709165 -0.7723912 65.2909165 -35.7250592
351 352 353 354 355 356 357 358 359 360
-75.2297489 -13.3255935 6.6020309 1.3796746 33.9105050 -32.1809667 -6.7667829 8.3255494 1.7227538 6.2954538
361 362 363 364 365 366 367 368 369 370
-13.1121767 -18.4779148 -26.1228502 -40.7330177 -40.2995927 11.1992519 -53.4330177 12.7206927 15.9438283 0.8744662
371 372 373 374 375 376 377 378 379 380
14.6274350 12.1849237 17.9903191 17.5831017 9.7050104 -1.6750536 -21.0323741 10.4915904 14.1275816 -2.6599213
381 382 383 384 385 386 387 388 389 390
56.3874121 -20.7412754 0.8660832 -6.9490274 -4.0515302 3.1081973 -26.8574120 -6.2705227 -25.5500218 18.7202317
391 392 393 394 395 396 397 398 399 400
26.6071210 -9.3621246 18.6562058 15.2346198 24.5500433 -14.5917340 -23.5475702 5.3365852 -11.1855493 -17.6451907
401 402 403 404 405 406 407 408 409 410
-27.0326823 -31.0294709 4.3147072 6.0070527 -31.2415860 -21.5565793 7.7822483 28.1001003 -14.2310850 -17.9680129
411 412 413 414 415 416 417 418 419 420
23.6684555 -12.9228347 16.4666695 23.5281003 16.0484555 4.8919871 -5.9640830 -4.0229752 -22.2311029 -10.7567553
421 422 423 424 425 426 427 428 429 430
-32.3911029 -4.8300887 6.0848413 14.7276026 -3.6044363 -6.9467553 -27.7818254 2.7899113 45.8781746 23.7666282
431 432 433 434 435 436 437 438 439 440
26.9154602 0.6847328 36.2586676 22.9199616 11.6754602 5.7786178 16.5699616 16.1204602 10.0869010 28.6386676
441 442 443 444 445 446 447 448 449 450
17.2086178 7.6799616 12.8899924 -29.9575666 -25.3056943 -42.3005133 -53.7164167 -34.0669887 -0.4395571 16.0667866
451 452 453 454 455 456 457 458 459 460
12.4422852 24.7588424 -5.7312238 48.0022852 3.4974429 17.6542866 37.0115578 -23.7581999 8.5314403 -7.4245073
461 462 463 464 465 466 467 468 469 470
3.7514429 8.4467866 -13.0372635 12.2718442 20.3708593 13.5813974 15.6909941 -10.1737176 23.6830285 2.4997061
471 472 473 474 475 476 477 478 479 480
8.6195936 15.9550717 7.4298882 15.5050080 -15.2135547 -15.2537176 -0.0137176 -2.7329715 -18.8181510 -24.1155585
481 482 483 484 485 486 487 488 489 490
-4.3145785 -5.1168976 -7.4895785 -15.5943976 -9.8653009 -54.9858729 13.8935492 -12.1885785 -4.1008976 -25.1053009
491 492 493 494 495 496 497 498 499 500
-21.9658729 -46.5534926 -5.3195585 16.4731024 -24.5058729 20.6265487 43.0584210 23.8411019 4.4876986 -23.2762067
501 502 503 504 505 506 507 508 509 510
25.0138214 5.4261019 -17.7728734 35.8665487 31.6284210 -4.7338981 -34.2828734 11.3194410 38.9145487 15.1184210
511 512 513 514 515 516 517 518 519 520
-19.9458326 -0.6960771 9.6861096 24.8418533 19.3315144 30.2165385 3.7489229 -6.8238904 30.8108533 0.9165144
521 522 523 524 525 526 527 528 529 530
-15.7138904 10.2298477 -7.5421900 -3.0124992 -6.6227437 6.2994429 -54.5331467 75.9365385 35.2710911 32.1031581
531 532 533 534 535 536 537 538 539 540
30.4399961 -4.5186524 32.5887348 31.5374684 15.6349125 12.4839913 49.5130308 30.5484454 -0.3247165 30.1216350
541 542 543 544 545 546 547 548 549 550
51.9106745 48.7110618 36.6929427 25.7822930 1.4455504 -26.9252928 -39.6160134 11.9689715 47.8643951 8.7529427
551 552 553 554 555 556 557 558 559 560
33.8943951 8.7529427 23.4213914 -10.8515376 -19.1289091 -24.1633100 -14.3681195 -16.2112777 -9.6763414 -18.9743099
561 562 563 564 565 566 567 568 569 570
-30.7156404 -27.0123380 -6.5069896 -29.3010480 -39.5574420 -38.9162725 6.0985544 -4.6829574 -6.9266738 -27.1233301
571 572 573 574 575 576 577 578 579 580
45.0919430 19.9959611 7.9550844 21.2332392 -15.6892847 -20.0368536 -2.3118514 8.5659611 -39.5764193 -58.2873423
581 582 583 584 585 586 587 588 589 590
-18.9000572 -46.2905449 -19.3619972 -14.9216474 1.4024515 -14.2409685 -39.2200572 -13.2705449 18.7380028 -34.0826469
591 592 593 594 595 596 597 598 599 600
-5.5402152 9.2540315 -8.4860572 -18.3505449 -4.1219972 -6.7080572 -23.4305449 12.3880028 -7.4126469 31.9949581
601 602 603 604 605 606 607 608 609 610
20.0024727 -3.3325206 -3.4342312 -0.4740029 11.1641661 24.5773413 3.5441661 -2.5159920 -16.7758339 -23.0476587
611 612 613 614 615 616 617 618 619 620
-40.9268758 -11.1744672 28.1296965 37.8425806 42.0920299 51.4647719 -18.3928951 -18.5807780 -1.9502618 5.8742987
621 622 623 624 625 626 627 628 629 630
4.2111367 -12.3325118 9.1186090 10.4187804 15.6765671 11.1020364 15.6765671 15.5047031 17.8401034 -2.3339254
631 632 633 634 635 636 637 638 639 640
24.1065756 48.4801623 -33.5191061 10.7228834 -19.3676776 -47.3036075 -6.2104499 -15.7391061 -11.7436075 -25.2604001
641 642 643 644 645 646 647 648 649 650
-23.5671166 -24.6319839 3.9691276 -23.8957754 -25.0264036 -15.7505541 -31.8559085 16.4040915 31.0535973 -15.4942370
651 652 653 654 655 656 657 658 659 660
7.3450218 -0.0919812 -32.6839531 -23.9504288 12.7265653 44.0551098 30.9079939 75.8709460 50.1003670 54.1385897
661 662 663 664 665 666 667 668 669 670
48.9927535 20.3035755 38.6703670 45.2485897 27.0769089 -15.0458489 -6.9689626 7.0152212 -8.7957798 -3.8371399
671 672 673 674 675 676 677 678 679 680
-35.1733455 -14.5493274 22.2479507 -14.9496399 -33.4800122 -19.6293274 -20.4216327 37.4879507 -52.7745732 -21.6171399
681 682 683 684 685 686 687 688 689 690
-36.5626608 16.6748393 -10.6325828 -1.6164038 5.8388105 -1.7910059 18.5873266 16.5800087 9.5095211 -20.9887244
691 692 693 694 695 696 697 698 699 700
17.3858278 24.9785419 18.1040087 1.8895211 39.9895211 -37.6028776 -21.3968957 -45.1077652 17.0742314 26.0298701
701 702 703 704 705 706 707 708 709 710
26.0065624 -4.9735467 -13.8234783 1.5104741 3.4390824 -3.8222283 4.1269153 8.9359937 0.6941222 24.9645485
711 712 713 714 715 716 717 718 719 720
36.8475028 -44.4324972 -23.8813731 -37.8727733 -33.5329124 -41.1989726 -36.6100007 -25.4181184 -24.3566771 -1.0747996
721 722 723 724 725 726 727 728 729 730
10.6810798 -19.3885468 74.0375450 53.7337634 56.0285847 29.0976379 8.8751266 35.4238554 5.8066380 39.7468075
731 732 733 734 735 736 737 738 739 740
33.0134712 8.8751266 12.5638554 10.5607325 5.6229595 1.1742887 2.5172781 -2.2416924 -45.7757036 12.5802042
741 742 743 744 745 746 747 748 749 750
11.3029942 -9.5665020 -15.0884257 -24.7118506 -45.8582127 -21.9995442 -22.4181828 -23.3165095 9.0443530 -18.7552918
751 752 753 754 755 756 757 758 759 760
0.1686364 0.1543530 -33.1486252 0.9185009 -13.8156470 -13.6752918 -27.0214991 27.9335555 7.1018303 -5.4292802
761 762 763 764 765 766 767 768 769 770
4.8431694 28.0051883 19.5743635 20.0288429 21.5128320 4.4460072 2.0184568 -12.7689740 5.1860269 -20.8466506
771 772 773 774 775 776 777 778 779 780
23.4029271 35.3950719 31.1650729 35.7478263 20.6935937 -1.9429281 46.9342395 43.3678263 7.1818464 11.2109271
781 782 783 784 785 786 787 788 789 790
19.3930719 29.5775729 41.8438263 29.4367104 -22.9012308 -26.1118028 -4.7569478 -15.4963016 -43.7993011 -36.5582869
791 792 793 794 795 796 797 798 799 800
-55.2766902 20.2334426 -4.7528785 1.0119168 -13.0284800 -39.8682786 -51.8714368 21.2064191 15.2199714 13.9499216
801 802 803 804 805 806 807 808 809 810
28.1279321 16.4760366 12.4524905 -29.3231362 12.5774856 46.5191952 0.3839324 3.5178577 -14.1861899 -6.6610094
811 812 813 814 815 816 817 818 819 820
-1.2464926 -18.4342621 -12.4277720 -8.6589656 -23.7528910 5.1691351 -13.4504552 -14.0926218 -6.0867723 18.4589094
821 822 823 824 825 826 827 828 829 830
-10.9104552 -10.2826218 72.6532277 121.9973791 -18.5304552 1.1473782 9.1532277 5.1239094 10.2562115 6.7353782
831 832 833 834 835 836 837 838 839 840
10.5949238 -22.4250762 4.2155105 -21.9976032 -10.9337132 -15.4560403 -23.9909401 -30.8574474 -43.6264264 -35.3008679
841 842 843 844 845 846 847 848 849 850
-29.8791615 -10.9959481 -7.1665760 7.4675304 28.8881335 12.6460722 11.6575386 -18.6220020 -26.0416952 -20.9222947
851 852 853 854 855 856 857 858 859 860
-23.1009887 -16.0240515 -40.1146510 -8.0033450 -57.6014145 -13.6264078 -5.6878901 -13.2257013 -18.1712825 -12.5826706
861 862 863 864 865 866 867 868 869 870
-18.0524509 -28.9782857 -41.5841798 -37.1823992 -41.7482078 -35.2792460 -62.2124049 -20.2066323 11.8633628 4.7936427
871 872 873 874 875 876 877 878 879 880
-2.6011082 -23.4975845 -20.2607427 6.1390738 2.7841937 -21.7165979 -11.1196009 -15.7715728 16.8128343 -21.7737137
881 882 883 884 885 886 887 888 889 890
-13.5772688 -4.6289191 3.0343111 -3.0709505 -5.6296382 -3.5318709 11.7497854 0.1168101 -27.1858675 21.4764239
891 892 893 894 895 896 897 898 899 900
10.7117312 -2.7950242 -5.4910674 -17.6502976 8.4033238 -10.6690962 27.2168151 16.5948750 -22.2557746 11.7004492
901 902 903 904 905 906 907 908 909 910
-6.0795508 -6.1436892 -57.7148650 14.0030355 3.4413679 -0.5097840 -4.3609776 -13.8989804 -2.3099031 3.1578597
911 912 913 914 915 916 917 918 919 920
3.5143217 -26.0854586 -23.7731796 -15.7202758 -31.9750714 -56.1462650 4.1657322 23.1051958 -40.6887934 -14.4777362
921 922 923 924 925 926 927 928 929 930
-16.6365521 -40.7257940 -28.3217170 -37.3598336 -9.1886985 -30.1775078 26.6909185 -20.7665021 -5.5559476 51.0591733
931 932 933 934 935 936 937 938 939 940
-11.8711140 -35.0399227 -39.3132669 -32.4194250 -44.5836421 -13.7444472 68.3986210 -2.0781637 0.4216735 25.8305933
941 942 943 944 945 946 947 948 949 950
1.8985376 19.6785376 7.6033088 5.0311912 -5.2944231 -9.2556634 -1.4881291 -23.3159639 5.9117696 32.6962943
951 952 953 954 955 956 957 958 959 960
37.9902834 33.6234586 15.0451031 10.2298397 45.9820021 48.7441888 53.1049325 47.9574507 37.2706177 26.5033841
961 962 963 964 965 966 967 968 969 970
48.3222466 33.2820021 7.0881888 47.5945936 48.9546177 0.5909013 23.5572466 35.8220021 -27.4558112 20.7314861
971 972 973 974 975 976 977 978 979 980
-24.6844958 15.6514861 29.2076917 2.6491566 -8.6546644 12.4432336 0.9558233 -55.3861167 -0.3141767 10.8186690
981 982 983 984 985 986 987 988 989 990
-0.3823102 1.3186804 -11.1946644 20.1929969 1.5310639 -17.6580980 -25.3117465 7.4929969 11.9450639 -9.6147647
991 992 993 994 995 996 997 998 999 1000
-17.6917465 20.1929969 18.6067592 24.2559230 14.4050784 0.8580928 7.0055641 14.5709984 -9.1915656 54.1667592
[ reached 'max' / getOption("max.print") -- omitted 8746 entries ]plot(perfla.lm)
Post-hoc comparisons
perfla.emm <- emmeans(perfla.lm, ~ begin_date_year*age_group)
pairs(perfla.emm, simple = "age_group")begin_date_year = 1988:
contrast estimate SE df t.ratio p.value
age_group0 - age_group1 -34.2 2.52 9719 -13.574 <.0001
age_group0 - age_group2 -72.1 2.44 9719 -29.599 <.0001
age_group0 - age_group3 -100.5 2.42 9719 -41.538 <.0001
age_group0 - age_group4 -126.1 2.43 9719 -51.863 <.0001
age_group0 - age_group5 -151.6 2.46 9719 -61.594 <.0001
age_group0 - age_group6 -174.5 2.51 9719 -69.369 <.0001
age_group0 - age_group7 -195.7 2.64 9719 -74.074 <.0001
age_group0 - age_group8 -214.7 2.86 9719 -74.938 <.0001
age_group0 - age_group9 -231.2 3.27 9719 -70.623 <.0001
age_group0 - age_group10 -243.2 3.96 9719 -61.402 <.0001
age_group0 - age_group11 -258.1 5.43 9719 -47.487 <.0001
age_group1 - age_group2 -37.9 1.26 9719 -29.957 <.0001
age_group1 - age_group3 -66.3 1.23 9719 -53.959 <.0001
age_group1 - age_group4 -91.9 1.25 9719 -73.645 <.0001
age_group1 - age_group5 -117.4 1.30 9719 -90.080 <.0001
age_group1 - age_group6 -140.2 1.40 9719 -100.040 <.0001
age_group1 - age_group7 -161.5 1.62 9719 -99.845 <.0001
age_group1 - age_group8 -180.5 1.96 9719 -92.006 <.0001
age_group1 - age_group9 -196.9 2.52 9719 -78.187 <.0001
age_group1 - age_group10 -209.0 3.36 9719 -62.114 <.0001
age_group1 - age_group11 -223.9 5.02 9719 -44.627 <.0001
age_group2 - age_group3 -28.4 1.03 9719 -27.616 <.0001
age_group2 - age_group4 -54.0 1.05 9719 -51.363 <.0001
age_group2 - age_group5 -79.5 1.12 9719 -71.210 <.0001
age_group2 - age_group6 -102.4 1.23 9719 -83.191 <.0001
age_group2 - age_group7 -123.6 1.47 9719 -84.021 <.0001
age_group2 - age_group8 -142.6 1.84 9719 -77.359 <.0001
age_group2 - age_group9 -159.1 2.43 9719 -65.518 <.0001
age_group2 - age_group10 -171.1 3.30 9719 -51.904 <.0001
age_group2 - age_group11 -186.0 4.97 9719 -37.415 <.0001
age_group3 - age_group4 -25.6 1.01 9719 -25.398 <.0001
age_group3 - age_group5 -51.1 1.07 9719 -47.562 <.0001
age_group3 - age_group6 -73.9 1.19 9719 -62.050 <.0001
age_group3 - age_group7 -95.2 1.44 9719 -66.170 <.0001
age_group3 - age_group8 -114.2 1.82 9719 -62.827 <.0001
age_group3 - age_group9 -130.6 2.41 9719 -54.264 <.0001
age_group3 - age_group10 -142.7 3.28 9719 -43.477 <.0001
age_group3 - age_group11 -157.6 4.96 9719 -31.760 <.0001
age_group4 - age_group5 -25.5 1.09 9719 -23.334 <.0001
age_group4 - age_group6 -48.4 1.21 9719 -40.013 <.0001
age_group4 - age_group7 -69.6 1.45 9719 -47.923 <.0001
age_group4 - age_group8 -88.6 1.83 9719 -48.456 <.0001
age_group4 - age_group9 -105.1 2.42 9719 -43.495 <.0001
age_group4 - age_group10 -117.1 3.29 9719 -35.624 <.0001
age_group4 - age_group11 -132.0 4.97 9719 -26.586 <.0001
age_group5 - age_group6 -22.9 1.26 9719 -18.079 <.0001
age_group5 - age_group7 -44.1 1.50 9719 -29.421 <.0001
age_group5 - age_group8 -63.1 1.87 9719 -33.824 <.0001
age_group5 - age_group9 -79.6 2.44 9719 -32.566 <.0001
age_group5 - age_group10 -91.6 3.31 9719 -27.695 <.0001
age_group5 - age_group11 -106.5 4.98 9719 -21.392 <.0001
age_group6 - age_group7 -21.2 1.58 9719 -13.406 <.0001
age_group6 - age_group8 -40.2 1.93 9719 -20.800 <.0001
age_group6 - age_group9 -56.7 2.50 9719 -22.724 <.0001
age_group6 - age_group10 -68.8 3.35 9719 -20.543 <.0001
age_group6 - age_group11 -83.6 5.00 9719 -16.713 <.0001
age_group7 - age_group8 -19.0 2.09 9719 -9.066 <.0001
age_group7 - age_group9 -35.5 2.62 9719 -13.532 <.0001
age_group7 - age_group10 -47.5 3.44 9719 -13.807 <.0001
age_group7 - age_group11 -62.4 5.07 9719 -12.312 <.0001
age_group8 - age_group9 -16.5 2.84 9719 -5.795 <.0001
age_group8 - age_group10 -28.5 3.62 9719 -7.894 <.0001
age_group8 - age_group11 -43.4 5.19 9719 -8.370 <.0001
age_group9 - age_group10 -12.1 3.94 9719 -3.058 0.0928
age_group9 - age_group11 -26.9 5.42 9719 -4.969 <.0001
age_group10 - age_group11 -14.9 5.86 9719 -2.537 0.3167
P value adjustment: tukey method for comparing a family of 12 estimates test(pairs(perfla.emm, by = "begin_date_year"), by = NULL, adjust = "mvt") contrast begin_date_year estimate SE df t.ratio p.value
age_group0 - age_group1 1988 -34.2 2.52 9719 -13.574 <.0001
age_group0 - age_group2 1988 -72.1 2.44 9719 -29.599 <.0001
age_group0 - age_group3 1988 -100.5 2.42 9719 -41.538 <.0001
age_group0 - age_group4 1988 -126.1 2.43 9719 -51.863 <.0001
age_group0 - age_group5 1988 -151.6 2.46 9719 -61.594 <.0001
age_group0 - age_group6 1988 -174.5 2.51 9719 -69.369 <.0001
age_group0 - age_group7 1988 -195.7 2.64 9719 -74.074 <.0001
age_group0 - age_group8 1988 -214.7 2.86 9719 -74.938 <.0001
age_group0 - age_group9 1988 -231.2 3.27 9719 -70.623 <.0001
age_group0 - age_group10 1988 -243.2 3.96 9719 -61.402 <.0001
age_group0 - age_group11 1988 -258.1 5.43 9719 -47.487 <.0001
age_group1 - age_group2 1988 -37.9 1.26 9719 -29.957 <.0001
age_group1 - age_group3 1988 -66.3 1.23 9719 -53.959 <.0001
age_group1 - age_group4 1988 -91.9 1.25 9719 -73.645 <.0001
age_group1 - age_group5 1988 -117.4 1.30 9719 -90.080 <.0001
age_group1 - age_group6 1988 -140.2 1.40 9719 -100.040 <.0001
age_group1 - age_group7 1988 -161.5 1.62 9719 -99.845 <.0001
age_group1 - age_group8 1988 -180.5 1.96 9719 -92.006 <.0001
age_group1 - age_group9 1988 -196.9 2.52 9719 -78.187 <.0001
age_group1 - age_group10 1988 -209.0 3.36 9719 -62.114 <.0001
age_group1 - age_group11 1988 -223.9 5.02 9719 -44.627 <.0001
age_group2 - age_group3 1988 -28.4 1.03 9719 -27.616 <.0001
age_group2 - age_group4 1988 -54.0 1.05 9719 -51.363 <.0001
age_group2 - age_group5 1988 -79.5 1.12 9719 -71.210 <.0001
age_group2 - age_group6 1988 -102.4 1.23 9719 -83.191 <.0001
age_group2 - age_group7 1988 -123.6 1.47 9719 -84.021 <.0001
age_group2 - age_group8 1988 -142.6 1.84 9719 -77.359 <.0001
age_group2 - age_group9 1988 -159.1 2.43 9719 -65.518 <.0001
age_group2 - age_group10 1988 -171.1 3.30 9719 -51.904 <.0001
age_group2 - age_group11 1988 -186.0 4.97 9719 -37.415 <.0001
age_group3 - age_group4 1988 -25.6 1.01 9719 -25.398 <.0001
age_group3 - age_group5 1988 -51.1 1.07 9719 -47.562 <.0001
age_group3 - age_group6 1988 -73.9 1.19 9719 -62.050 <.0001
age_group3 - age_group7 1988 -95.2 1.44 9719 -66.170 <.0001
age_group3 - age_group8 1988 -114.2 1.82 9719 -62.827 <.0001
age_group3 - age_group9 1988 -130.6 2.41 9719 -54.264 <.0001
age_group3 - age_group10 1988 -142.7 3.28 9719 -43.477 <.0001
age_group3 - age_group11 1988 -157.6 4.96 9719 -31.760 <.0001
age_group4 - age_group5 1988 -25.5 1.09 9719 -23.334 <.0001
age_group4 - age_group6 1988 -48.4 1.21 9719 -40.013 <.0001
age_group4 - age_group7 1988 -69.6 1.45 9719 -47.923 <.0001
age_group4 - age_group8 1988 -88.6 1.83 9719 -48.456 <.0001
age_group4 - age_group9 1988 -105.1 2.42 9719 -43.495 <.0001
age_group4 - age_group10 1988 -117.1 3.29 9719 -35.624 <.0001
age_group4 - age_group11 1988 -132.0 4.97 9719 -26.586 <.0001
age_group5 - age_group6 1988 -22.9 1.26 9719 -18.079 <.0001
age_group5 - age_group7 1988 -44.1 1.50 9719 -29.421 <.0001
age_group5 - age_group8 1988 -63.1 1.87 9719 -33.824 <.0001
age_group5 - age_group9 1988 -79.6 2.44 9719 -32.566 <.0001
age_group5 - age_group10 1988 -91.6 3.31 9719 -27.695 <.0001
age_group5 - age_group11 1988 -106.5 4.98 9719 -21.392 <.0001
age_group6 - age_group7 1988 -21.2 1.58 9719 -13.406 <.0001
age_group6 - age_group8 1988 -40.2 1.93 9719 -20.800 <.0001
age_group6 - age_group9 1988 -56.7 2.50 9719 -22.724 <.0001
age_group6 - age_group10 1988 -68.8 3.35 9719 -20.543 <.0001
age_group6 - age_group11 1988 -83.6 5.00 9719 -16.713 <.0001
age_group7 - age_group8 1988 -19.0 2.09 9719 -9.066 <.0001
age_group7 - age_group9 1988 -35.5 2.62 9719 -13.532 <.0001
age_group7 - age_group10 1988 -47.5 3.44 9719 -13.807 <.0001
age_group7 - age_group11 1988 -62.4 5.07 9719 -12.312 <.0001
age_group8 - age_group9 1988 -16.5 2.84 9719 -5.795 <.0001
age_group8 - age_group10 1988 -28.5 3.62 9719 -7.894 <.0001
age_group8 - age_group11 1988 -43.4 5.19 9719 -8.370 <.0001
age_group9 - age_group10 1988 -12.1 3.94 9719 -3.058 0.0735
age_group9 - age_group11 1988 -26.9 5.42 9719 -4.969 0.0001
age_group10 - age_group11 1988 -14.9 5.86 9719 -2.537 0.2683
P value adjustment: mvt method for 66 tests #export tables
# #interpret(eta_squared(perfla.lm), rules = "cohen1992") %>%
# write.csv(file = "Outputs/Tables/perfla_eta_squared.csv")
#slope contrasts
# Obtain slope estimates
perfla.slopes <- emtrends(perfla.lm, ~age_group, var = "begin_date_year")
# Compare estimated slopes to 0
perfla.slope.contrasts <- test(perfla.slopes) %>%
mutate(Species = "Yellow Perch") %>%
rename(Age = age_group)
perfla.slope.contrasts %>%
write.csv(file = "Outputs/Tables/perfla_emmeans.csv")Plot raw data
(perfla.length.year.plot <- ggplot(data = perfla %>%
mutate(Age = as.factor(age_group)),
aes(x = begin_date_year, y = length_mean_mm,
color = Age, fill = Age, group = Age))+
#geom_point()+
geom_smooth(method = "lm")+
stat_poly_eq(use_label("eq"))+
scale_color_viridis_d()+
scale_fill_viridis_d()+
labs(y = "Length (mm)", x = "Year")+
theme_bw()
)`geom_smooth()` using formula = 'y ~ x'
Marginal Effects
result <- predict_response(perfla.lm, c("begin_date_year", "age_group"))
# plot(result)
# test_predictions(result) %>%
# write.csv(file = "Outputs/Tables/perfla_pairwise_length_time_slopes.csv", row.names = F)
(perfla.marginal.plot <- ggpredict(perfla.lm, terms = c("begin_date_year", "age_group")) %>%
plot()+
#stat_poly_eq(use_label("eq"))+
stat_regline_equation()+
stat_poly_line()+
# annotate(geom = "text", label = "y = 77 + 0.07x", x = 2000, y = 224)+
# annotate(geom = "text", label = "y = 21 + 0.093x", x = 2000, y = 212)+
# annotate(geom = "text", label = "y = -64 + 0.13x", x = 2000, y = 199)+
# annotate(geom = "text", label = "y = 230 - 0.028x", x = 2000, y = 182)+
# annotate(geom = "text", label = "y = 610 - 0.23x", x = 2000, y = 160)+
# annotate(geom = "text", label = "y = 920 - 0.39x", x = 2000, y = 137)+
# annotate(geom = "text", label = "y = 1200 - 0.55x", x = 2000, y = 110)+
# annotate(geom = "text", label = "y = 1700 - 0.83x", x = 2000, y = 80)+
scale_color_viridis_d()+
scale_fill_viridis_d()+
labs(title = NULL,
y = "Mean Length-at-Age (mm)",
x = "Year")+
guides(fill = guide_legend(title = "Age", reverse = T), color = guide_legend(title = "Age", reverse = T))+
theme_bw()
)Scale for colour is already present.
Adding another scale for colour, which will replace the existing scale.ggsave(filename = "~/GitHub/EAGER_growth/Outputs/Figures/Linear Models/perfla_marginal_effects_plot.tiff",
perfla.marginal.plot,
dpi = 300, width = 200, height = 150, units = "mm")
All species marginal effects plot
all.spp.marginal.effects <- ggarrange(corart.marginal.plot,
oncmyk.marginal.plot,
saltru.marginal.plot,
perfla.marginal.plot,
esoluc.marginal.plot,
sanvit.marginal.plot,
catcom.marginal.plot,
pomnig.marginal.plot,
ambrup.marginal.plot,
micdol.marginal.plot,
lepgib.marginal.plot,
lepmac.marginal.plot,
common.legend = T,
legend = "right")
all.spp.marginal.effects
salmond.marginal.effects <- ggarrange(corart.marginal.plot,
oncmyk.marginal.plot,
saltru.marginal.plot,
ncol = 1,
common.legend = T,
legend = "right")Synthesis Plots Across Species
Create tibble of slopes of all species size classes
+1
[1] 1Some summary statistics
#mean slope per thermal guild
mean.mm.yr.thermal.guild <- all.slopes %>%
filter(`p-value` == "≤ 0.05") %>%
group_by(`Thermal Guild`) %>%
summarise(mean_Slope = mean(Slope), mean_Percent = mean(`Percent Change`))
#per species
mean.mm.yr.species <- all.slopes %>%
filter(`p-value` == "≤ 0.05") %>%
group_by(Species) %>%
summarise(mean_Slope = mean(Slope), mean_Percent = mean(`Percent Change`))
mean.mm.yr.species.lifestage <- all.slopes %>%
filter(`p-value` == "≤ 0.05") %>%
group_by(Species, `Life Stage`) %>%
summarise(mean_Slope = mean(Slope), mean_Percent = mean(`Percent Change`))`summarise()` has grouped output by 'Species'. You can override using the `.groups` argument.mean.mm.year.life.stage <- all.slopes %>%
filter(`p-value` == "≤ 0.05") %>%
group_by(`Thermal Guild`, `Life Stage`) %>%
summarise(mean_Slope = mean(Slope), mean_Percent = mean(`Percent Change`)) %>%
arrange(mean_Slope)`summarise()` has grouped output by 'Thermal Guild'. You can override using the `.groups` argument.mean.mm.age <- all.slopes %>%
filter(`p-value` == "≤ 0.05") %>%
group_by(Age) %>%
summarise(mean_Slope = mean(Slope), mean_Percent = mean(`Percent Change`)) %>%
arrange(mean_Slope)
#number of species age classes statistically increasing
all.slopes %>%
filter(`p-value` == "≤ 0.05") %>%
filter(Slope > 0) %>%
nrow()[1] 15#15
all.slopes %>%
#filter(`p-value` == "≤ 0.05") %>%
filter(Slope < 0,
`Thermal Guild` == "Warm") %>%
nrow()[1] 28#28
#number of species age classes statistically decreasing
all.slopes %>%
filter(`p-value` == "≤ 0.05") %>%
filter(Slope < 0) %>%
nrow()[1] 52#52##Plot
all.slopes.pc.CI <- all.slopes %>%
group_by(Species) %>%
summarize(
mean = mean(`Percent Change`),
sd = sd(`Percent Change`),
n = n(),
se = sd / sqrt(n),
ci_lower = mean - qt(0.975, n - 1) * se,
ci_upper = mean + qt(0.975, n - 1) * se
)
(
slopes.all.plot.percent <- ggplot() +
geom_hline(
yintercept = 0,
color = "red",
linetype = "dashed"
) +
#geom_boxplot(fill = "white", color = "black") +
geom_errorbar(data = all.slopes.pc.CI,
aes(x = Species, ymin = ci_lower, ymax = ci_upper),
width = 0.2) +
geom_point(data = all.slopes.pc.CI, aes(x = Species, y = mean), size = 3) +
geom_point(
data = all.slopes ,
aes(
x = Species,
y = `Percent Change`,
color = `Thermal Guild`,
fill = `Thermal Guild`,
shape = `p-value`,
group = Species
),
size = 2
) +
# scale_x_discrete(limits = c("Cisco", "Rainbow Trout", "Brown Trout", "Black Crappie",
# "Rock Bass", "Walleye", "Yellow Perch", "Northern Pike", "White Sucker",
# "Largemouth Bass", "Smallmouth Bass", "Pumpkinseed Sunfish", "Bluegill"))+
scale_color_viridis_d() +
scale_fill_viridis_d() +
scale_shape_manual(values = c(2, 16)) +
geom_text_repel(data = all.slopes,
aes(x = Species, y = `Percent Change`, label = Age),
show.legend = F,
max.overlaps = 100,
max.iter = 100000
) +
# annotate(
# geom = "text",
# x = 1,
# y = 0.9,
# label = 0.18,
# size = 3
# ) +
# annotate(
# geom = "text",
# x = 2,
# y = 0.9,
# label = 0.51,
# size = 3
# ) +
# annotate(
# geom = "text",
# x = 4,
# y = 0.9,
# label = -0.23,
# size = 3
# ) +
# annotate(
# geom = "text",
# x = 5,
# y = 0.9,
# label = -0.30,
# size = 3
# ) +
# annotate(
# geom = "text",
# x = 6,
# y = 0.9,
# label = -0.15,
# size = 3
# ) +
# annotate(
# geom = "text",
# x = 7,
# y = 0.9,
# label = -0.16,
# size = 3
# ) +
# annotate(
# geom = "text",
# x = 8,
# y = 0.9,
# label = -0.24,
# size = 3
# ) +
# annotate(
# geom = "text",
# x = 9,
# y = 0.9,
# label = -0.45,
# size = 3
# ) +
# annotate(
# geom = "text",
# x = 10,
# y = 0.9,
# label = -0.32,
# size = 3
# ) +
# annotate(
# geom = "text",
# x = 11,
# y = 0.9,
# label = -0.07,
# size = 3
# ) +
# annotate(
# geom = "text",
# x = 12,
# y = 0.9,
# label = -0.24,
# size = 3
# ) +
# annotate(
# geom = "text",
# x = 13,
# y = 0.9,
# label = -0.21,
# size = 3
# ) +
labs(y = "Annual Percent Change in Length-at-Age", x = NULL) +
theme_bw()+
theme(
#panel.background = element_rect(fill = "gray"),
,
axis.text.x = element_text(
angle = 45,
hjust = 1,
size = 12
),
axis.text.y = element_text(size = 10),
axis.title.x = element_text(size = 12)
)
)
ggsave(filename = "~/GitHub/EAGER_growth/Outputs/Figures/Linear Models/all_slopes_percent_thermal.tiff",
slopes.all.plot.percent,
dpi = 300, width = 200, height = 150, units = "mm")
Shape by life stage
all.slopes.life.stage.CI <- all.slopes %>%
group_by(Species, `Life Stage`) %>%
summarize(
mean = mean(`Percent Change`),
sd = sd(`Percent Change`),
n = n(),
se = sd / sqrt(n),
ci_lower = mean - qt(0.975, n - 1) * se,
ci_upper = mean + qt(0.975, n - 1) * se
) %>%
mutate(`Life Stage` = fct_rev(`Life Stage`))Warning: There were 4 warnings in `summarize()`.
The first warning was:
ℹ In argument: `ci_lower = mean - qt(0.975, n - 1) * se`.
ℹ In group 1: `Species = Cisco` `Life Stage = "Adult"`.
Caused by warning in `qt()`:
! NaNs produced
ℹ Run ]8;;ide:run:dplyr::last_dplyr_warnings()dplyr::last_dplyr_warnings()]8;; to see the 3 remaining warnings.`summarise()` has grouped output by 'Species'. You can override using the `.groups` argument.(
slopes.all.plot.percent.life.stage <- ggplot() +
geom_hline(
yintercept = 0,
color = "red",
linetype = "dashed"
) +
# geom_boxplot(
# fill = "white",
# color = "black",
# outliers = F
# ) +
geom_errorbar(
data = all.slopes.life.stage.CI,
aes(
x = Species,
ymin = ci_lower,
ymax = ci_upper,
group = interaction(`Life Stage`, Species)
),
position = position_dodge(width = 0.4),
width = 0.2
) +
geom_point(data = all.slopes.life.stage.CI,
aes(
x = Species,
y = mean,
group = interaction(`Life Stage`, Species)
),
position = position_dodge(width = 0.4),
size = 3) +
geom_jitter(
data = all.slopes %>%
mutate(`Life Stage` = fct_rev(`Life Stage`)),
aes(
x = Species,
y = `Percent Change`,
color = `Thermal Guild`,
fill = `Thermal Guild`,
shape = `p-value`,
group = interaction(`Life Stage`, Species)
),
position = position_dodge(width = 0.4),
size = 2
) +
# scale_x_discrete(limits = c("Cisco", "Rainbow Trout", "Brown Trout", "Black Crappie",
# "Rock Bass", "Walleye", "Yellow Perch", "Northern Pike", "White Sucker",
# "Largemouth Bass", "Smallmouth Bass", "Pumpkinseed Sunfish", "Bluegill"))+
scale_color_viridis_d() +
scale_fill_viridis_d() +
scale_shape_manual(values = c(2, 16)) +
labs(y = "Annual Percent Change in Length-at-Age", x = NULL) +
# annotate(
# geom = "text",
# x = 0.75,
# y = 0.3,
# label = 0.18,
# size = 2
# ) +
# annotate(
# geom = "text",
# x = 2,
# y = 0.6,
# label = 0.51,
# size = 2
# ) +
# annotate(
# geom = "text",
# x = 3.75,
# y = 0.1,
# label = -0.31,
# size = 2
# ) +
# annotate(
# geom = "text",
# x = 4.25,
# y = 0.2,
# label = -0.11,
# size = 2
# ) +
# annotate(
# geom = "text",
# x = 4.75,
# y = 0.1,
# label = -0.26,
# size = 2
# ) +
# annotate(
# geom = "text",
# x = 5.25,
# y = 0.01,
# label = -0.33,
# size = 2
# ) +
# annotate(
# geom = "text",
# x = 5.75,
# y = 0.15,
# label = -0.36,
# size = 2
# ) +
# annotate(
# geom = "text",
# x = 6.25,
# y = 0.25,
# label = -0.10,
# size = 2
# ) +
# annotate(
# geom = "text",
# x = 6.75,
# y = 0.1,
# label = -0.21,
# size = 2
# ) +
# annotate(
# geom = "text",
# x = 7.25,
# y = 0.2,
# label = -0.08,
# size = 2
# ) +
# annotate(
# geom = "text",
# x = 7.75,
# y = -0.03,
# label = -0.24,
# size = 2
# ) +
# annotate(
# geom = "text",
# x = 8.75,
# y = 0.1,
# label = -0.45,
# size = 2
# ) +
# annotate(
# geom = "text",
# x = 9.75,
# y = 0.15,
# label = -0.32,
# size = 2
# ) +
# annotate(
# geom = "text",
# x = 10.75,
# y = 0.01,
# label = -0.54,
# size = 2
# ) +
# annotate(
# geom = "text",
# x = 11.25,
# y = 0.25,
# label = 0.12,
# size = 2
# ) +
# annotate(
# geom = "text",
# x = 11.75,
# y = -0.03,
# label = -0.30,
# size = 2
# ) +
# annotate(
# geom = "text",
# x = 12.25,
# y = 0.05,
# label = -0.17,
# size = 2
# ) +
# annotate(
# geom = "text",
# x = 12.75,
# y = 0.15,
# label = -0.45,
# size = 2
# ) +
# annotate(
# geom = "text",
# x = 13.25,
# y = 0.25,
# label = 0.09,
# size = 2
# ) +
theme_bw()+
theme(
#panel.background = element_rect(fill = "gray"),
,
axis.text.x = element_text(
angle = 45,
hjust = 1,
size = 12
),
axis.text.y = element_text(size = 10),
axis.title.x = element_text(size = 12)
)
)
ggsave(filename = "~/GitHub/EAGER_growth/Outputs/Figures/Linear Models/all_slopes_percent_thermal_life_stage.tiff",
slopes.all.plot.percent.life.stage,
dpi = 300, width = 200, height = 150, units = "mm")
Break adults and juveniles into their own panels Juveniles
(
slopes.all.plot.percent.juvenile <- ggplot(data = all.slopes.life.stage.CI %>%
filter(`Life Stage` == "Juvenile")) +
geom_hline(
yintercept = 0,
color = "red",
linetype = "dashed"
) +
geom_errorbar(
aes(
x = Species,
ymin = ci_lower,
ymax = ci_upper,
group = interaction(`Life Stage`, Species)
),
position = position_dodge(width = 0.4),
width = 0.2
) +
geom_point(
aes(
x = Species,
y = mean,
group = interaction(`Life Stage`, Species)
),
position = position_dodge(width = 0.4),
size = 3) +
geom_jitter(
data = all.slopes %>%
filter(`Life Stage` == "Juvenile"),
aes(
x = Species,
y = `Percent Change`,
color = `Thermal Guild`,
fill = `Thermal Guild`,
shape = `p-value`,
#group = interaction(`Life Stage`, Species)
),
position = position_dodge(width = 0.4),
size = 2
) +
scale_color_viridis_d() +
scale_fill_viridis_d() +
scale_shape_manual(values = c(2, 16)) +
labs(y = "Annual Percent Change in Length-at-Age",
x = NULL,
title = "Juveniles") +
theme_bw()+
theme(
#panel.background = element_rect(fill = "gray"),
,
axis.text.x = element_text(
angle = 45,
hjust = 1,
size = 12
),
axis.text.y = element_text(size = 10),
axis.title.x = element_text(size = 12)
)
)
Juveniles
(
slopes.all.plot.percent.adult <- ggplot(data = all.slopes.life.stage.CI %>%
filter(`Life Stage` == "Adult")) +
geom_hline(
yintercept = 0,
color = "red",
linetype = "dashed"
) +
geom_errorbar(
aes(
x = Species,
ymin = ci_lower,
ymax = ci_upper,
group = interaction(`Life Stage`, Species)
),
position = position_dodge(width = 0.4),
width = 0.2
) +
geom_point(
aes(
x = Species,
y = mean,
group = interaction(`Life Stage`, Species)
),
position = position_dodge(width = 0.4),
size = 3) +
geom_jitter(
data = all.slopes %>%
filter(`Life Stage` == "Adult"),
aes(
x = Species,
y = `Percent Change`,
color = `Thermal Guild`,
fill = `Thermal Guild`,
shape = `p-value`,
#group = interaction(`Life Stage`, Species)
),
position = position_dodge(width = 0.4),
size = 2
) +
scale_color_viridis_d() +
scale_fill_viridis_d() +
scale_shape_manual(values = c(2, 16)) +
labs(y = "Annual Percent Change in Length-at-Age",
x = NULL,
title = "Adults") +
theme_bw()+
theme(
#panel.background = element_rect(fill = "gray"),
,
axis.text.x = element_text(
angle = 45,
hjust = 1,
size = 12
),
axis.text.y = element_text(size = 10),
axis.title.x = element_text(size = 12)
)
)
Plot by age class instead of species
all.slopes.age.CI <- all.slopes %>%
group_by(Age) %>%
summarize(
mean = mean(`Percent Change`),
sd = sd(`Percent Change`),
n = n(),
se = sd / sqrt(n),
ci_lower = mean - qt(0.975, n - 1) * se,
ci_upper = mean + qt(0.975, n - 1) * se
)
(
slopes.all.plot.percent.age.class <- ggplot() +
geom_hline(
yintercept = 0,
color = "red",
linetype = "dashed"
) +
# geom_boxplot(
# fill = "white",
# color = "black",
# outliers = F
# ) +
geom_errorbar(
data = all.slopes.age.CI,
aes(
x = Age,
ymin = ci_lower,
ymax = ci_upper
),
position = position_dodge(width = 0.4),
width = 0.2
) +
geom_point(data = all.slopes.age.CI,
aes(
x = Age,
y = mean,
),
position = position_dodge(width = 0.4),
size = 3) +
geom_jitter(data = all.slopes ,
aes(
x = Age,
y = `Percent Change`,
color = `Thermal Guild`,
fill = `Thermal Guild`,
shape = `p-value`
),
width = 0.2,
size = 2) +
#geom_text(aes(label = spp_code))+
# scale_x_discrete(limits = c("Cisco", "Rainbow Trout", "Brown Trout", "Black Crappie",
# "Rock Bass", "Walleye", "Yellow Perch", "Northern Pike", "White Sucker",
# "Largemouth Bass", "Smallmouth Bass", "Pumpkinseed Sunfish", "Bluegill"))+
scale_color_viridis_d() +
scale_fill_viridis_d() +
scale_shape_manual(values = c(2, 16)) +
labs(y = "Annual Percent Change in Length-at-Age") +
# annotate(
# geom = "text",
# x = 1,
# y = 0.05,
# label = -0.66,
# size = 3
# ) +
# annotate(
# geom = "text",
# x = 2,
# y = 0.25,
# label = -0.57,
# size = 3
# ) +
# annotate(
# geom = "text",
# x = 3,
# y = 0.5,
# label = -0.18,
# size = 3
# ) +
# annotate(
# geom = "text",
# x = 4,
# y = 0.5,
# label = -0.08,
# size = 3
# ) +
# annotate(
# geom = "text",
# x = 5,
# y = 0.5,
# label = 0.04,
# size = 3
# ) +
# annotate(
# geom = "text",
# x = 6,
# y = 0.5,
# label = 0.01,
# size = 3
# ) +
# annotate(
# geom = "text",
# x = 7,
# y = 0.5,
# label = -0.03,
# size = 3
# ) +
# annotate(
# geom = "text",
# x = 8,
# y = 0.5,
# label = -0.09,
# size = 3
# ) +
# annotate(
# geom = "text",
# x = 9,
# y = 0.5,
# label = -0.12,
# size = 3
# ) +
# annotate(
# geom = "text",
# x = 10,
# y = 0.5,
# label = -0.23,
# size = 3
# ) +
# annotate(
# geom = "text",
# x = 11,
# y = 0.5,
# label = -0.21,
# size = 3
# ) +
# annotate(
# geom = "text",
# x = 13,
# y = 0.5,
# label = -0.18,
# size = 3
# ) +
theme_bw()+
theme(
#panel.background = element_rect(fill = "gray"),
#axis.text.x = element_text(angle = 45, hjust = 1, size = 12),
axis.text.y = element_text(size = 10),
axis.title.x = element_text(size = 12)
)
)
ggsave(filename = "~/GitHub/EAGER_growth/Outputs/Figures/Linear Models/all_slopes_percent_age_class.tiff",
slopes.all.plot.percent.age.class,
dpi = 300, width = 200, height = 150, units = "mm")
(slopes.all.plot.percent.age.class.spp.facet <- ggplot(data = all.slopes ,
aes(x = Age, y = `Percent Change`,
color = `Thermal Guild`,
fill = `Thermal Guild`,
shape = `p-value`,
group = Age
))+
geom_hline(yintercept = 0, color = "red", linetype = "dashed")+
geom_point(size = 2)+
facet_wrap(~Species)+
scale_color_viridis_d()+
scale_fill_viridis_d()+
scale_shape_manual(values = c(2, 16))+
labs(y = "Annual Percent Change in Length-at-Age")+
theme_bw()+
theme(#panel.background = element_rect(fill = "gray"),
#axis.text.x = element_text(angle = 45, hjust = 1, size = 12),
axis.text.y = element_text(size = 8), axis.title.x = element_text(size = 10),
axis.text.x = element_text(size = 8),
strip.text = element_text(size = 8))
)
ggsave(filename = "~/GitHub/EAGER_growth/Outputs/Figures/Linear Models/all_slopes_percent_age_class_spp_facet.tiff",
slopes.all.plot.percent.age.class.spp.facet,
dpi = 300, width = 200, height = 150, units = "mm")
Multi-panel figure to include in text that shows trends by species and by age class
age.class.species.multi.panel.plot <- ggarrange(slopes.all.plot.percent.age.class,
slopes.all.plot.percent.age.class.spp.facet,
slopes.all.plot.percent,
slopes.all.plot.percent.life.stage,
ncol = 2, nrow = 2,
common.legend = T, legend = "right",
labels = "AUTO", label.x = 0.04)
age.class.species.multi.panel.plot
ggsave(filename = "~/GitHub/EAGER_growth/Outputs/Figures/Linear Models/age_class_species_multi-panel_plot.tiff",
age.class.species.multi.panel.plot,
dpi = 300, width = 267, height = 200, units = "mm")
Two-panel version
age.class.species.two.panel.plot <- ggarrange(
slopes.all.plot.percent.age.class,
slopes.all.plot.percent.life.stage,
ncol = 1,
nrow = 2,
common.legend = T,
legend = "right",
labels = "AUTO",
label.x = 0.03
)
age.class.species.two.panel.plot
ggsave(filename = "~/GitHub/EAGER_growth/Outputs/Figures/Linear Models/age_class_species_two-panel_plot.tiff",
age.class.species.two.panel.plot,
dpi = 300, width = 200, height = 250, units = "mm")
Model of slopes with FTP
all slopes
Visualize the relationships
#Check assumptions
#linear relationships
(slopes.linear.plot <- ggscatter(data = all.slopes, x = "FTP", y = "Percent Change", color = "Life Stage", add = "reg.line")+
stat_regline_equation(aes(label = paste(..eq.label.., ..rr.label.., sep = "~~~~"), color = `Life Stage`))+
scale_color_viridis_d(end = 0.8)
)
# ggsave(filename = "~/GitHub/EAGER_growth/Outputs/Figures/Linear Models/all_slopes_linear_relationships.tiff",
# slopes.linear.plot,
# dpi = 300, width = 200, height = 150, units = "mm")Check assumptions
#homogeneity of regression slopes
anova_test(`Percent Change` ~ FTP*`Life Stage`, data = all.slopes) # not idealANOVA Table (type II tests)
Effect DFn DFd F p p<.05 ges
1 FTP 1 121 5.845 1.70e-02 * 0.046
2 `Life Stage` 1 121 14.296 2.44e-04 * 0.106
3 FTP:`Life Stage` 1 121 18.567 3.36e-05 * 0.133slopes.model <- lm(`Percent Change` ~ FTP*`Life Stage`, data = all.slopes)
summary(slopes.model)
Call:
lm(formula = `Percent Change` ~ FTP * `Life Stage`, data = all.slopes)
Residuals:
Min 1Q Median 3Q Max
-0.67369 -0.09492 0.02072 0.11876 0.76197
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -0.371961 0.161382 -2.305 0.022879 *
FTP 0.012759 0.006545 1.949 0.053578 .
`Life Stage`Juvenile 0.690630 0.197746 3.493 0.000669 ***
FTP:`Life Stage`Juvenile -0.035534 0.008247 -4.309 3.36e-05 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.2093 on 121 degrees of freedom
Multiple R-squared: 0.2267, Adjusted R-squared: 0.2075
F-statistic: 11.82 on 3 and 121 DF, p-value: 7.605e-07slopes.model.metric <- augment(slopes.model)
#normality of residuals
slopes.shapiro <- shapiro_test(slopes.model.metric$.resid)
slopes.shapiro # p < 0.05 hist(scale(all.slopes$`Percent Change`))
#homogeneity of variances
slopes.levene <- slopes.model.metric %>% levene_test(.resid ~ `Life Stage`)Warning in leveneTest.default(y = y, group = group, ...) :
group coerced to factor.slopes.levene # p = 0.43 => meets assumptions
#outliers
slopes.model.metric %>%
filter(abs(.std.resid) > 3) %>%
as.data.frame() #no outliers
res.aov <- all.slopes %>% anova_test(`Percent Change` ~ FTP + `Life Stage`)
get_anova_table(res.aov)ANOVA Table (type II tests)
Effect DFn DFd F p p<.05 ges
1 FTP 1 122 5.109 0.026000 * 0.040
2 `Life Stage` 1 122 12.497 0.000577 * 0.093pwc <- all.slopes %>%
rename(life_stage = `Life Stage`,
percent_change = `Percent Change`) %>%
emmeans_test(
percent_change ~ life_stage, covariate = FTP,
p.adjust.method = "bonferonni"
)
pwc
get_emmeans(pwc)Visualize pairwise comparisons
# Visualization: line plots with p-values
pwc <- pwc %>% add_xy_position(x = "life_stage", fun = "mean_se")
(slopes.ancova.emmeans.plot <- ggline(get_emmeans(pwc), x = "life_stage", y = "emmean") +
geom_errorbar(aes(ymin = conf.low, ymax = conf.high), width = 0.2) +
stat_pvalue_manual(pwc, hide.ns = TRUE, tip.length = FALSE) +
labs(
subtitle = get_test_label(res.aov, detailed = TRUE),
caption = get_pwc_label(pwc),
x = "Life Stage", y = "Estimated Marginal Means")
)
# ggsave(filename = "~/GitHub/EAGER_growth/Outputs/Figures/Linear Models/all_slopes_percent_age_class_spp_facet.tiff",
# slopes.ancova.emmeans.plot,
# dpi = 300, width = 200, height = 150, units = "mm")Stack plots
all.slopes.ancova.stack <- ggarrange(slopes.linear.plot, slopes.ancova.emmeans.plot,
ncol = 1, nrow = 2,
labels = "AUTO")
all.slopes.ancova.stack
ggsave(filename = "~/GitHub/EAGER_growth/Outputs/Figures/Linear Models/all_slopes_ancova_stack.tiff",
all.slopes.ancova.stack,
dpi = 300, width = 200, height = 150, units = "mm")
sig diff from 0 slopes
Visualize the relationship
all.slopes.sig.dif <- all.slopes %>% filter(p.value < 0.05)
#calculate quantiles
all.slopes.sig.dif.quants <- quantile(all.slopes.sig.dif$`Percent Change`, c(0.025, 0.975))
#identify points outside 2.5% and 97.5% quantiles
all.slopes.sig.dif <- all.slopes.sig.dif %>%
mutate(Outlier = case_when(
Slope < -0.8107416 ~ "Below 2.5%",
Slope > -0.8107416 & Slope < 0.3922495 ~ "Not outliers",
Slope > 0.3922495 ~ "Above 97.5%"
))
#linear relationships
(slopes.sig.dif.linear.plot <- ggscatter(data = all.slopes.sig.dif,
x = "FTP", y = "Percent Change",
color = "Life Stage", add = "reg.line"
#, shape = "Outlier"
)+
stat_regline_equation(aes(label = paste(..eq.label.., ..rr.label.., sep = "~~~~"), color = `Life Stage`))+
scale_color_viridis_d(end = 0.8)+
scale_shape_manual(values = c(1, 2, 19))
)
Check assumptions
#homogeneity of regression slopes
anova_test(`Percent Change` ~ FTP*`Life Stage`, data = all.slopes.sig.dif) # not idealANOVA Table (type II tests)
Effect DFn DFd F p p<.05 ges
1 FTP 1 63 4.917 3.00e-02 * 0.072
2 `Life Stage` 1 63 8.232 6.00e-03 * 0.116
3 FTP:`Life Stage` 1 63 18.016 7.33e-05 * 0.222slopes.model.sig.dif <- lm(`Percent Change` ~ FTP*`Life Stage`, data = all.slopes.sig.dif)
summary(slopes.model.sig.dif)
Call:
lm(formula = `Percent Change` ~ FTP * `Life Stage`, data = all.slopes.sig.dif)
Residuals:
Min 1Q Median 3Q Max
-0.61891 -0.14777 0.03025 0.16239 0.70916
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -0.66489 0.27257 -2.439 0.017543 *
FTP 0.02302 0.01088 2.115 0.038367 *
`Life Stage`Juvenile 1.21377 0.33301 3.645 0.000543 ***
FTP:`Life Stage`Juvenile -0.05724 0.01349 -4.244 7.33e-05 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.2462 on 63 degrees of freedom
Multiple R-squared: 0.3207, Adjusted R-squared: 0.2884
F-statistic: 9.915 on 3 and 63 DF, p-value: 1.919e-05slopes.model.metric.sig.dif <- augment(slopes.model.sig.dif)
#normality of residuals
slopes.shapiro.sig.dif <- shapiro_test(slopes.model.metric.sig.dif$.resid)
slopes.shapiro.sig.dif # p = 0.001 => violates assumptions
#homogeneity of variances
slopes.levene.sig.dif <- slopes.model.metric.sig.dif %>% levene_test(.resid ~ `Life Stage`)Warning in leveneTest.default(y = y, group = group, ...) :
group coerced to factor.slopes.levene.sig.dif # p = 0.17 => meets assumptions
#outliers
slopes.model.metric.sig.dif %>%
filter(abs(.std.resid) > 3) %>%
as.data.frame() #two outliers
res.aov.sig.dif <- all.slopes.sig.dif %>% anova_test(`Percent Change` ~ FTP + `Life Stage`)
get_anova_table(res.aov.sig.dif)ANOVA Table (type II tests)
Effect DFn DFd F p p<.05 ges
1 FTP 1 64 3.885 0.053 0.057
2 `Life Stage` 1 64 6.503 0.013 * 0.092pwc.sig.dif <- all.slopes.sig.dif %>%
rename(life_stage = `Life Stage`,
percent_change = `Percent Change`) %>%
emmeans_test(
percent_change ~ life_stage, covariate = FTP,
p.adjust.method = "bonferroni"
)
pwc.sig.dif
get_emmeans(pwc.sig.dif)Visuallize pairwise comparisons
# Visualization: line plots with p-values
pwc.sig.dif <- pwc.sig.dif %>% add_xy_position(x = "life_stage", fun = "mean_se")
(slopes.sig.dif.ancova.emmeans.plot <- ggline(get_emmeans(pwc.sig.dif), x = "life_stage", y = "emmean") +
geom_errorbar(aes(ymin = conf.low, ymax = conf.high), width = 0.2) +
stat_pvalue_manual(pwc.sig.dif, hide.ns = TRUE, tip.length = FALSE) +
labs(
subtitle = get_test_label(res.aov.sig.dif, detailed = TRUE),
caption = get_pwc_label(pwc.sig.dif),
x = "Life Stage", y = "Estimated Marginal Means")
)
stack plots
slopes.sig.dif.ancova.stack <- ggarrange(slopes.sig.dif.linear.plot, slopes.sig.dif.ancova.emmeans.plot,
ncol = 1, nrow = 2,
labels = "AUTO", legend = "right")
slopes.sig.dif.ancova.stack
ggsave(filename = "~/GitHub/EAGER_growth/Outputs/Figures/Linear Models/sig_dif_slopes_ancova_stack.tiff",
slopes.sig.dif.ancova.stack,
dpi = 300, width = 200, height = 150, units = "mm")
Multi-panel plot for lm and ancova
All species
Model
Filter for target species from age groups that had enough data for a BRT, remove NA rows
#linear model
all.spp.lm <- lm(length_mean_mm ~ begin_date_year*age_group*species + log_max_depth + logarea + doy,
data = grow.merge.target.spp)
summary(all.spp.lm)
options(na.action = "na.omit")
dd <- dredge(all.spp.lm)
#calculate and interpret effect sizes
eta_squared(all.spp.lm)
#interpret(eta_squared(all.spp.lm), rules = "cohen1992")
#calculate AIC score
AIC(all.spp.lm)
#examine model fit
testDispersion(all.spp.lm)
simulation.output <- simulateResiduals(fittedModel = all.spp.lm)
residuals(all.spp.lm)
residuals(all.spp.lm, quantileFunction = qnorm)
plot(all.spp.lm)Post-hoc comparisons
all.spp.emm <- emmeans(all.spp.lm, ~ begin_date_year*age_group*species)
pairs(all.spp.emm, by = c("species", "age_group", "begin_date_year"))
test(pairs(all.spp.emm, by = c("species", "age_group", "begin_date_year")), by = NULL, adjust = "mvt")
#export tables
#interpret(eta_squared(all.spp.lm), rules = "cohen1992") %>%
write.csv(file = "Outputs/Tables/all_spp_eta_squared.csv")
#slope contrasts
# Obtain slope estimates
all.spp.slopes <- emtrends(all.spp.lm, ~age_group*species, var = "begin_date_year")
# Compare estimated slopes to 0
all.spp.slope.contrasts <- test(all.spp.slopes) %>%
rename(Age = age_group) %>%
filter(!is.na(`p.value`))
all.spp.slope.contrasts %>%
write.csv(file = "Outputs/Tables/all_spp_emmeans.csv")Plot raw data
(all.spp.length.year.plot <- ggplot(data = grow.merge.target.spp %>%
mutate(Age = as.factor(age_group)),
aes(x = begin_date_year, y = length_mean_mm,
color = Age, fill = Age, group = Age))+
facet_wrap(~species, scales = "free")+
#geom_point()+
geom_smooth(method = "lm")+
stat_poly_eq(use_label("eq"))+
scale_color_viridis_d()+
scale_fill_viridis_d()+
labs(y = "Length (mm)", x = "Year")+
theme_bw()
)
Marginal Effects
result <- predict_response(all.spp.lm, c("begin_date_year", "age_group", "species"))Bayesian model
Building a single model that includes all species using species as a fixed effect
Trouble Shooting
library(cmdstanr)
install_cmdstan()
cmdstan_path()
Model setup
nrow(filter(grow.merge.target.spp, Data_Type == "Historic"))
[1] 54559Run models
Fit the models
# fit1 <- brm(
# formula = formula1,
# data = grow.merge.target.spp,
# family = gaussian(),
# chains = 4,
# iter = 2000,
# cores = 4,
# control = list(adapt_delta = 0.95)
# )
fit2 <- brm(
formula = formula2,
data = grow.merge.target.spp,
family = gaussian(),
chains = 4,
iter = 100000,
thin = 10,
cores = 4,
control = list(adapt_delta = 0.9)
)Warning: Rows containing NAs were excluded from the model.
Compiling Stan program...
Start sampling
Warning: There were 20000 transitions after warmup that exceeded the maximum treedepth. Increase max_treedepth above 10. See
https://mc-stan.org/misc/warnings.html#maximum-treedepth-exceeded
Warning: Examine the pairs() plot to diagnose sampling problems# fit3 <- brm(
# formula = formula3,
# data = grow.merge.target.spp,
# family = gaussian(),
# chains = 4,
# iter = 2000,
# cores = 4,
# control = list(adapt_delta = 0.95)
# )Examine results
summary(fit2)Error in h(simpleError(msg, call)) :
error in evaluating the argument 'object' in selecting a method for function 'summary': object 'fit2' not foundExtract random slopes
ranef(fit2)
$species_age_group
, , Intercept
Estimate Est.Error Q2.5 Q97.5
black_crappie.0 -144.089182 638.7384 -1387.107734 1113.64355
bluegill.0 225.940455 584.2351 -918.880199 1369.96282
largemouth_bass.0 849.147913 349.9536 156.498561 1528.53373
northern_pike.0 2921.473753 485.7860 1983.088015 3872.10111
smallmouth_bass.0 -262.785109 613.3290 -1469.730689 939.15875
walleye.0 664.372355 726.2681 -748.892457 2097.47055
yellow_perch.0 780.665217 378.5249 42.878510 1518.45365
black_crappie.1 556.301990 245.4427 79.711363 1039.24879
bluegill.1 886.731732 165.1430 562.703734 1211.44154
brown_trout.1 1482.810018 840.6901 -153.402526 3126.08077
common_white_sucker.1 1041.754057 765.7504 -433.179331 2536.70334
largemouth_bass.1 1555.890243 156.9409 1248.022984 1864.67479
northern_pike.1 2466.006950 179.7469 2112.460378 2815.39729
pumpkinseed_sunfish.1 574.552925 228.6931 122.777289 1024.64382
rainbow_trout.1 -81.114849 490.7823 -1033.508396 897.32771
rock_bass.1 611.113078 470.5594 -307.941290 1525.14159
smallmouth_bass.1 1210.851117 268.6599 679.714773 1738.14040
walleye.1 1557.885753 312.5764 942.607477 2169.26825
yellow_perch.1 774.271377 160.5447 458.809627 1088.85954
black_crappie.2 415.455447 168.5724 86.420311 746.93683
bluegill.2 489.737373 125.7711 242.775090 734.51423
brown_trout.2 1179.716617 595.1059 4.451528 2337.78413
cisco.2 275.792941 554.9407 -813.127039 1365.55983
common_white_sucker.2 1118.351550 576.3380 -11.728922 2256.18979
largemouth_bass.2 1040.774232 128.7364 788.630500 1293.54227
northern_pike.2 1689.171048 137.8491 1419.799508 1959.23400
pumpkinseed_sunfish.2 152.374571 147.5406 -137.511977 445.65329
rainbow_trout.2 -1939.090415 434.1100 -2794.102787 -1096.64450
rock_bass.2 218.368937 239.5486 -250.052953 686.71349
smallmouth_bass.2 457.468969 212.6033 38.903748 876.83432
walleye.2 484.091898 274.6525 -51.797223 1016.68312
yellow_perch.2 699.829639 125.8933 450.357073 945.52342
black_crappie.3 103.820618 166.5711 -225.477793 430.18219
bluegill.3 281.776869 114.4683 56.406483 506.01091
brown_trout.3 462.818427 563.2304 -620.182954 1570.27323
cisco.3 416.958750 483.1436 -539.950313 1354.95321
common_white_sucker.3 2473.773601 461.1999 1567.650854 3386.25544
largemouth_bass.3 801.654050 122.2244 563.897931 1042.43975
northern_pike.3 1187.806712 138.9134 914.343028 1461.62960
pumpkinseed_sunfish.3 -280.432354 127.2882 -532.094741 -31.86330
rainbow_trout.3 -2056.890451 491.0814 -3023.823787 -1091.06724
rock_bass.3 -43.294233 197.4814 -431.228567 340.72510
smallmouth_bass.3 14.249673 203.4099 -381.385505 412.90735
walleye.3 -52.201766 261.4630 -566.761870 464.02996
yellow_perch.3 305.194766 115.6495 79.883486 529.25827
black_crappie.4 -182.015600 178.7491 -531.575721 165.22811
bluegill.4 -30.523436 117.6359 -258.600360 200.52616
brown_trout.4 -89.569583 696.7158 -1447.547143 1284.21497
cisco.4 397.779742 415.2899 -420.028617 1214.94531
common_white_sucker.4 2262.543582 485.8215 1309.453819 3206.40571
largemouth_bass.4 650.241793 135.3274 387.898565 911.01444
northern_pike.4 2092.195638 152.7314 1793.754209 2390.23573
pumpkinseed_sunfish.4 -553.614841 133.4737 -812.985666 -290.84013
rainbow_trout.4 -4684.820657 695.6058 -6067.909614 -3329.49056
rock_bass.4 -198.785743 188.5975 -570.520307 167.78864
smallmouth_bass.4 -270.332629 222.7403 -707.497882 166.21583
walleye.4 -356.499432 271.4621 -890.228802 169.13600
yellow_perch.4 158.248832 122.5541 -78.776982 398.19497
black_crappie.5 -473.336043 204.1047 -879.093034 -76.88918
bluegill.5 -408.174345 129.5233 -662.731367 -153.96719
brown_trout.5 992.678232 811.9658 -595.597184 2592.72489
cisco.5 -732.703860 506.9795 -1716.111004 272.51232
common_white_sucker.5 1644.435939 568.7038 527.339980 2761.65104
largemouth_bass.5 793.704307 155.7550 488.196260 1094.94047
northern_pike.5 3049.974222 171.0861 2714.140445 3383.49960
pumpkinseed_sunfish.5 -694.894672 150.7967 -986.759742 -395.21878
rock_bass.5 -162.387011 200.3897 -557.272358 235.15874
smallmouth_bass.5 -264.919062 253.6156 -757.785254 236.77493
walleye.5 -156.896976 272.1406 -697.600525 374.24946
yellow_perch.5 316.623533 138.2931 47.707822 588.38534
black_crappie.6 -320.362204 230.1914 -768.175586 134.31778
bluegill.6 -653.418620 142.2809 -931.181756 -371.80626
cisco.6 137.422445 469.9266 -785.161363 1049.84849
largemouth_bass.6 1257.958215 179.0514 907.226406 1608.49561
northern_pike.6 4456.992787 197.6461 4068.084075 4841.26874
pumpkinseed_sunfish.6 -671.938880 187.2714 -1035.476866 -304.72401
rock_bass.6 -345.460935 224.1703 -786.455399 93.91406
smallmouth_bass.6 -199.847700 278.9669 -744.042852 346.02901
walleye.6 -629.966272 281.0352 -1175.161590 -76.54421
yellow_perch.6 482.393824 166.0884 154.855029 805.16543
black_crappie.7 17.441843 300.0642 -571.542886 604.22254
bluegill.7 -534.000799 171.3317 -865.126435 -195.79265
cisco.7 711.497865 667.0898 -589.233258 2032.43046
largemouth_bass.7 1761.971087 209.5673 1354.604768 2172.00033
northern_pike.7 5909.833142 255.5673 5407.670399 6407.28676
pumpkinseed_sunfish.7 -802.594109 242.7162 -1273.594378 -324.02534
rock_bass.7 -55.715033 246.3705 -541.095367 426.91718
smallmouth_bass.7 392.185383 317.6473 -230.651841 1011.24506
walleye.7 -4.971035 290.2358 -576.084789 563.66314
yellow_perch.7 792.872786 218.3163 371.107175 1221.06614
black_crappie.8 388.956274 393.7067 -373.630516 1165.63410
bluegill.8 -426.319974 234.6138 -884.904756 38.16233
largemouth_bass.8 1869.228038 255.1356 1371.013460 2375.46349
northern_pike.8 8294.624422 342.1700 7617.534732 8961.37807
pumpkinseed_sunfish.8 -698.229419 377.4960 -1439.861147 49.31636
rock_bass.8 -136.981185 288.0555 -697.801540 433.39323
smallmouth_bass.8 30.575449 361.2364 -685.270261 738.45445
walleye.8 769.967036 315.2162 163.203193 1401.71043
yellow_perch.8 175.284445 293.1095 -401.928979 745.99635
black_crappie.9 684.459394 518.3615 -335.595249 1685.33052
bluegill.9 -248.904148 331.6501 -897.872184 397.94251
largemouth_bass.9 1895.095408 309.8320 1292.310760 2505.17505
northern_pike.9 7739.649448 532.2071 6688.692032 8787.84237
pumpkinseed_sunfish.9 -330.147277 519.0587 -1352.860781 687.23385
rock_bass.9 -45.053177 362.2742 -753.585514 664.96589
smallmouth_bass.9 227.827798 459.4470 -672.851619 1142.68869
walleye.9 1180.328195 360.2989 469.359150 1875.39007
yellow_perch.9 126.281680 391.7756 -635.863129 897.80298
black_crappie.10 1150.579207 689.7317 -213.480768 2504.48369
bluegill.10 -158.166543 436.0190 -1012.444963 688.97023
largemouth_bass.10 1547.858728 371.7113 818.271385 2271.01693
northern_pike.10 4805.170228 684.1840 3462.249458 6122.81388
pumpkinseed_sunfish.10 -628.084116 820.5326 -2242.956220 962.73702
rock_bass.10 390.243876 462.8510 -516.464214 1288.45504
smallmouth_bass.10 911.017336 576.5210 -212.919063 2037.68450
walleye.10 2269.468425 399.4602 1495.546409 3058.41412
yellow_perch.10 512.749157 545.6074 -556.808125 1579.99778
black_crappie.11 486.190683 886.0174 -1234.754952 2227.38998
bluegill.11 69.129459 624.5064 -1151.063754 1281.94064
largemouth_bass.11 1270.362801 514.4399 262.132884 2281.55322
rock_bass.11 494.957187 678.6441 -819.565939 1838.27284
walleye.11 1146.695342 525.4429 119.511399 2177.58594
yellow_perch.11 51.887550 727.6435 -1382.430537 1486.62755
largemouth_bass.12 1486.914170 652.9372 204.350857 2763.33264
walleye.12 2148.078612 640.0154 891.566761 3391.79491
, , begin_date_year
Estimate Est.Error Q2.5 Q97.5
black_crappie.0 -0.0268130166 0.32213471 -0.66141913 0.599456001
bluegill.0 -0.2303325771 0.29430122 -0.80651547 0.346418369
largemouth_bass.0 -0.5301963154 0.17648684 -0.87201979 -0.180482967
northern_pike.0 -1.4897675182 0.24554639 -1.97129157 -1.016153309
smallmouth_bass.0 0.0328625783 0.30956785 -0.57443036 0.641191992
walleye.0 -0.3908290365 0.36513146 -1.11225388 0.320294202
yellow_perch.0 -0.4985324453 0.19081099 -0.87115750 -0.125109003
black_crappie.1 -0.3601021888 0.12360447 -0.60328746 -0.119732753
bluegill.1 -0.5507831656 0.08255629 -0.71253340 -0.388578248
brown_trout.1 -0.7750647337 0.42137241 -1.59881091 0.041797376
common_white_sucker.1 -0.5734823572 0.38690284 -1.32708891 0.172147802
largemouth_bass.1 -0.8543376196 0.07854884 -1.00886693 -0.700684895
northern_pike.1 -1.2004706422 0.09031330 -1.37546704 -1.022625709
pumpkinseed_sunfish.1 -0.3920737340 0.11481953 -0.61718358 -0.165815535
rainbow_trout.1 0.0270117280 0.24677073 -0.46290103 0.505739356
rock_bass.1 -0.4104498369 0.23583337 -0.86920144 0.050718436
smallmouth_bass.1 -0.6807835966 0.13466554 -0.94517339 -0.413694965
walleye.1 -0.8060639452 0.15674099 -1.11196163 -0.498191853
yellow_perch.1 -0.4779157810 0.08045708 -0.63529777 -0.319752668
black_crappie.2 -0.2664508142 0.08432937 -0.43241808 -0.101285696
bluegill.2 -0.3357371985 0.06284574 -0.45829429 -0.212709852
brown_trout.2 -0.5743597464 0.29928608 -1.15866879 0.015621326
cisco.2 -0.1595842545 0.28064675 -0.71075090 0.391101540
common_white_sucker.2 -0.5587727777 0.29134928 -1.13397629 0.012730290
largemouth_bass.2 -0.5622751156 0.06430585 -0.68952296 -0.437314606
northern_pike.2 -0.7520124803 0.06888506 -0.88705371 -0.617746894
pumpkinseed_sunfish.2 -0.1651707839 0.07385570 -0.31207291 -0.020824029
rainbow_trout.2 1.0030915096 0.21860851 0.57740416 1.433950350
rock_bass.2 -0.1983355313 0.11989966 -0.43285760 0.037288042
smallmouth_bass.2 -0.2673679470 0.10647649 -0.47621177 -0.057388325
walleye.2 -0.2233647433 0.13755599 -0.49076709 0.045041258
yellow_perch.2 -0.4212779923 0.06281152 -0.54361561 -0.296997887
black_crappie.3 -0.0910358906 0.08333621 -0.25462995 0.074749332
bluegill.3 -0.2170262115 0.05702822 -0.32818498 -0.105424273
brown_trout.3 -0.1687854537 0.28332619 -0.72584974 0.375933976
cisco.3 -0.2148422668 0.24444496 -0.69213444 0.268038748
common_white_sucker.3 -1.2067739355 0.23287356 -1.66743256 -0.747241003
largemouth_bass.3 -0.4141870699 0.06092917 -0.53334093 -0.295725754
northern_pike.3 -0.4625473700 0.06934280 -0.59844652 -0.325580412
pumpkinseed_sunfish.3 0.0658382826 0.06356907 -0.05857309 0.190737297
rainbow_trout.3 1.1033714160 0.24742486 0.61911472 1.590622191
rock_bass.3 -0.0513187289 0.09867511 -0.24351918 0.142121365
smallmouth_bass.3 -0.0135919100 0.10180860 -0.21370836 0.184483078
walleye.3 0.0757517613 0.13090692 -0.18211010 0.333807755
yellow_perch.3 -0.2084820464 0.05763075 -0.32067799 -0.096049922
black_crappie.4 0.0665846469 0.08934869 -0.10807364 0.242069013
bluegill.4 -0.0477697776 0.05872977 -0.16269918 0.066644493
brown_trout.4 0.1412664884 0.35056957 -0.55233535 0.821643117
cisco.4 -0.1870994888 0.20948964 -0.60067049 0.224555449
common_white_sucker.4 -1.0711599529 0.24536989 -1.54748151 -0.589784191
largemouth_bass.4 -0.3160776993 0.06754432 -0.44758473 -0.185353539
northern_pike.4 -0.8854212902 0.07630395 -1.03494134 -0.736220329
pumpkinseed_sunfish.4 0.2140674369 0.06662513 0.08198739 0.343878920
rainbow_trout.4 2.4727155070 0.34964304 1.79467341 3.168137250
rock_bass.4 0.0405907991 0.09440395 -0.14397483 0.225414353
smallmouth_bass.4 0.1575167132 0.11145409 -0.06054390 0.376451649
walleye.4 0.2541209243 0.13592016 -0.01134701 0.519651228
yellow_perch.4 -0.1216950256 0.06120881 -0.24103480 -0.002238420
black_crappie.5 0.2223443571 0.10202935 0.02430333 0.425409820
bluegill.5 0.1521626523 0.06452025 0.02595488 0.279019001
brown_trout.5 -0.3743106463 0.40865402 -1.18017246 0.427824756
cisco.5 0.3991568552 0.25593689 -0.10777346 0.894949562
common_white_sucker.5 -0.7484086265 0.28674849 -1.31157931 -0.182858012
largemouth_bass.5 -0.3694153277 0.07769749 -0.52006717 -0.216989292
northern_pike.5 -1.3390438488 0.08544773 -1.50658932 -1.172211828
pumpkinseed_sunfish.5 0.2936920682 0.07536018 0.14420080 0.439665979
rock_bass.5 0.0344124715 0.10020034 -0.16451450 0.232986987
smallmouth_bass.5 0.1761328226 0.12685604 -0.07473521 0.423028826
walleye.5 0.1734673095 0.13621387 -0.09235623 0.442616308
yellow_perch.5 -0.1886396117 0.06909030 -0.32322019 -0.054037251
black_crappie.6 0.1547843635 0.11507681 -0.07283975 0.379902968
bluegill.6 0.2833915322 0.07110765 0.14280029 0.422082633
cisco.6 -0.0322542214 0.23691067 -0.49414978 0.430071991
largemouth_bass.6 -0.5860129718 0.08940742 -0.76133491 -0.411721836
northern_pike.6 -2.0190927101 0.09887638 -2.21075313 -1.824944721
pumpkinseed_sunfish.6 0.2885733386 0.09367018 0.10370459 0.471064218
rock_bass.6 0.1368777378 0.11209198 -0.08332060 0.356936286
smallmouth_bass.6 0.1601575487 0.13955472 -0.11315560 0.432453459
walleye.6 0.4297976392 0.14082504 0.15367226 0.702440745
yellow_perch.6 -0.2607549180 0.08308978 -0.42347884 -0.096255278
black_crappie.7 -0.0070173946 0.15000504 -0.30127420 0.287623233
bluegill.7 0.2297059940 0.08559014 0.06117786 0.395236674
cisco.7 -0.3102679195 0.33640952 -0.97748777 0.344713811
largemouth_bass.7 -0.8223865373 0.10471996 -1.02770950 -0.617131967
northern_pike.7 -2.7255740302 0.12777386 -2.97451883 -2.473802147
pumpkinseed_sunfish.7 0.3595281465 0.12132415 0.12138358 0.595654004
rock_bass.7 0.0007047573 0.12330057 -0.24129464 0.242997197
smallmouth_bass.7 -0.1238889287 0.15889175 -0.43387043 0.187158552
walleye.7 0.1297975912 0.14520470 -0.15489875 0.416282927
yellow_perch.7 -0.4063905086 0.10947299 -0.62220064 -0.194948967
black_crappie.8 -0.1859175805 0.19663467 -0.57482994 0.195200190
bluegill.8 0.1812253753 0.11733926 -0.05079954 0.411626179
largemouth_bass.8 -0.8613915377 0.12747291 -1.11515038 -0.613193434
northern_pike.8 -3.8914098178 0.17128008 -4.22471414 -3.553783275
pumpkinseed_sunfish.8 0.3134020500 0.18851912 -0.05897299 0.682531097
rock_bass.8 0.0495018973 0.14400649 -0.23752051 0.329899404
smallmouth_bass.8 0.0680154686 0.18068073 -0.28553566 0.426142466
walleye.8 -0.2473482023 0.15773698 -0.56364802 0.057019318
yellow_perch.8 -0.0864163859 0.14708176 -0.37295145 0.201792200
black_crappie.9 -0.3282020194 0.25898891 -0.83126126 0.181819582
bluegill.9 0.0979814629 0.16587652 -0.22811870 0.422999732
largemouth_bass.9 -0.8610616872 0.15473195 -1.16514113 -0.560429884
northern_pike.9 -3.5757169719 0.26611960 -4.10010427 -3.052355887
pumpkinseed_sunfish.9 0.1330339428 0.25935407 -0.37620322 0.645221534
rock_bass.9 0.0099022375 0.18111232 -0.34385256 0.365395603
smallmouth_bass.9 -0.0208050660 0.22967948 -0.47773643 0.429490917
walleye.9 -0.4462175260 0.18031039 -0.79409725 -0.090545457
yellow_perch.9 -0.0538776033 0.19661145 -0.43824694 0.328120007
black_crappie.10 -0.5555214737 0.34446183 -1.23103366 0.124782807
bluegill.10 0.0568219599 0.21807300 -0.36717696 0.485060764
largemouth_bass.10 -0.6791599196 0.18567099 -1.03914489 -0.313915124
northern_pike.10 -2.1004939926 0.34143913 -2.75867434 -1.429779572
pumpkinseed_sunfish.10 0.2853897637 0.40980286 -0.51083152 1.092948478
rock_bass.10 -0.2029087384 0.23128540 -0.65027663 0.248968657
smallmouth_bass.10 -0.3546589703 0.28807945 -0.91921677 0.207057706
walleye.10 -0.9827839993 0.19992027 -1.37663490 -0.594750086
yellow_perch.10 -0.2422374037 0.27371845 -0.77956659 0.295227374
black_crappie.11 -0.2165859135 0.44150258 -1.08879638 0.642019236
bluegill.11 -0.0511108894 0.31262472 -0.66049358 0.560044087
largemouth_bass.11 -0.5324798886 0.25675071 -1.03769306 -0.028235838
rock_bass.11 -0.2509827834 0.33883798 -0.92308532 0.408703556
walleye.11 -0.4163508285 0.26287399 -0.93272577 0.096730612
yellow_perch.11 -0.0032169089 0.36515496 -0.72188946 0.717234383
largemouth_bass.12 -0.6377584224 0.32585223 -1.27659204 0.001110146
walleye.12 -0.9083114283 0.31930452 -1.52827874 -0.282789686#Use this chunk if you don't have the model saved in your environment and are reading back in the output that was previously written out to excel
group_effects <- read.xlsx("~/GitHub/EAGER_growth/Outputs/Tables/bayesian_model_output.xlsx", 2)
head(group_effects)
# Get posterior means for each group's slope
slopes <- as.data.frame(group_effects) %>%
rename_with(~str_remove(.x, "species_age_group.")) %>%
#rename columns to facilitate plotting
rename(species_age_group = X1,
slope_bayes = Estimate.begin_date_year,
lower_bayes = Q2.5.begin_date_year,
upper_bayes = Q97.5.begin_date_year) %>%
#split species_age_group into species and age_group for plotting
separate(species_age_group, into = c("species", "age_group"), sep = "\\.") %>%
#fix age_group order to be numeric
mutate(age_group = factor(age_group, levels = sort(as.numeric(unique(age_group))))) %>%
#change species names to remove _
mutate(species = species %>%
str_replace_all("_", " ") %>%
str_to_title()) %>%
#rename age_group to make plot cleaner
rename(Age = age_group, Species = species) %>%
#join with frequentist output
left_join(all.slopes %>%
mutate(Species = if_else(Species == "White Sucker", "Common White Sucker", Species)),
by = c("Species", "Age")) %>%
#reorder Species by FTP for plotting
mutate(Species = fct_reorder(Species, FTP)) %>%
#create percent change column for Bayes slopes
mutate(`Bayes Percent Change` = slope_bayes/mean_length*100,
bayes_pc_lower = lower_bayes/mean_length*100,
bayes_pc_upper = upper_bayes/mean_length*100,
#create column for if credibilty interval overlaps with 0
`Statistically Different` = if_else(lower_bayes > 0 | upper_bayes < 0, "Different from 0", "Overlaps 0")
)
Export output after joining with frequentist output
write.csv(slopes, "~/GitHub/EAGER_growth/Outputs/Tables/bayes_all_slopes.csv", row.names = F)Summarize by species life stage
slopes.age <- slopes %>%
group_by(Age) %>%
summarize(mean_slope = mean(slope_bayes), sd_slope = sd(slope_bayes),
mean_pc = mean(`Bayes Percent Change`), sd_pc = sd(`Bayes Percent Change`)) %>%
pivot_wider(names_from = `Life Stage`,
values_from = c(mean_slope, sd_slope, mean_pc, sd_pc)) %>%
select(Species, contains("Juvenile"), contains("Adult"))
Error in `pivot_wider()`:
! Can't select columns that don't exist.
✖ Column `Life Stage` doesn't exist.
Run `]8;;x-r-run:rlang::last_trace()rlang::last_trace()]8;;` to see where the error occurred.Custom plots
Percent change by age class facet by species
thermal.guild.colors <- c("#4662D7FF", "#1AE4B6FF", "#CB2A04FF")
(fit2.species.age.groups.plot <- ggplot(slopes, aes(x = Age, y = `Bayes Percent Change`,
color = `Thermal Guild`, fill = `Statistically Different`,
shape = `Statistically Different`)) +
facet_wrap(~Species)+
geom_hline(yintercept = 0, color = "red", linetype = "dashed")+
geom_point(position=position_dodge(width=0.5), size=0.5) +
geom_errorbar(aes(ymin=bayes_pc_lower, ymax=bayes_pc_upper),
width=0.2, position=position_dodge(width=0.5), alpha = 0.5) +
scale_color_manual(values = thermal.guild.colors)+
scale_shape_manual(values = c(19, 2))+
labs(
y = "Annual percent change relative to mean length (mm/yr)"
) +
theme_bw()+
theme(strip.text = element_text(size = 8))
)
ggsave(
ggsave(filename = "~/GitHub/EAGER_growth/Outputs/Figures/Linear Models/bayes_all_slopes_percent_age_class_spp_facet.tiff",
fit2.species.age.groups.plot,
dpi = 300, width = 200, height = 150, units = "mm")
)Species on x-axis with age labels
bayes.all.slopes.pc.CI <- slopes %>%
group_by(Species) %>%
summarize(
mean = mean(`Percent Change`),
sd = sd(`Percent Change`),
n = n(),
se = sd / sqrt(n),
ci_lower = mean - qt(0.975, n - 1) * se,
ci_upper = mean + qt(0.975, n - 1) * se
)
(
bayes.slopes.all.plot.percent <- ggplot() +
geom_hline(
yintercept = 0,
color = "red",
linetype = "dashed"
) +
geom_errorbar(data = bayes.all.slopes.pc.CI,
aes(x = Species, ymin = ci_lower, ymax = ci_upper),
width = 0.2) +
geom_point(data = all.slopes.pc.CI, aes(x = Species, y = mean), size = 3) +
geom_point(
data = slopes ,
aes(
x = Species,
y = `Percent Change`,
color = `Thermal Guild`,
fill = `Thermal Guild`,
shape = `Statistically Different`,
group = Species
),
size = 2
) +
scale_color_manual(values = thermal.guild.colors) +
scale_fill_manual(values = thermal.guild.colors) +
scale_shape_manual(values = c(2, 16)) +
geom_text_repel(data = slopes,
aes(x = Species, y = `Percent Change`, label = Age),
show.legend = F,
max.overlaps = 100,
max.iter = 100000
) +
labs(y = "Annual Percent Change in Length-at-Age", x = NULL) +
theme_bw()+
theme(
#panel.background = element_rect(fill = "gray"),
,
axis.text.x = element_text(
angle = 45,
hjust = 1,
size = 12
),
axis.text.y = element_text(size = 10),
axis.title.x = element_text(size = 12)
)
)
ggsave(filename = "~/GitHub/EAGER_growth/Outputs/Figures/Linear Models/bayes_slopes_percent_thermal.tiff",
bayes.slopes.all.plot.percent,
dpi = 300, width = 200, height = 150, units = "mm")Plot by species no facet
bayes.all.slopes.CI <- slopes %>%
group_by(Species) %>%
summarize(
mean = mean(slope_bayes),
sd = sd(slope_bayes),
n = n(),
se = sd / sqrt(n),
ci_lower = mean - qt(0.975, n - 1) * se,
ci_upper = mean + qt(0.975, n - 1) * se
)
(
bayes.slopes.all.plot <- ggplot() +
geom_hline(
yintercept = 0,
color = "red",
linetype = "dashed"
) +
geom_errorbar(
data = bayes.all.slopes.CI,
aes(x = Species, ymin = ci_lower, ymax = ci_upper),
width = 0.2
) +
geom_point(data = bayes.all.slopes.CI, aes(x = Species, y = mean), size = 3) +
geom_jitter(
data = slopes,
aes(
x = Species,
y = Slope,
color = `Thermal Guild`,
fill = `Thermal Guild`,
shape = `Statistically Different`,
group = Species
),
size = 2,
width = 0.2
) +
scale_color_manual(values = thermal.guild.colors) +
scale_fill_manual(values = thermal.guild.colors) +
scale_shape_manual(values = c(19, 2)) +
labs(y = "Change in Length-at-Age (mm/yr)", x = NULL) +
theme_bw()+
theme(
#panel.background = element_rect(fill = "gray"),
axis.text.x = element_text(angle = 45, hjust = 1, size = 12),
axis.text.y = element_text(size = 10),
axis.title.x = element_text(size = 12)
)
)
ggsave(filename = "~/GitHub/EAGER_growth/Outputs/Figures/Linear Models/bayes_all_slopes_species.tiff",
bayes.slopes.all.plot,
dpi = 300, width = 200, height = 150, units = "mm")Plot by age no facet
bayes.all.slopes.age.CI <- slopes %>%
group_by(Age) %>%
summarize(
mean = mean(`Bayes Percent Change`),
sd = sd(`Bayes Percent Change`),
n = n(),
se = sd / sqrt(n),
ci_lower = mean - qt(0.975, n - 1) * se,
ci_upper = mean + qt(0.975, n - 1) * se
)
(
bayes.slopes.all.age.percent.change.plot <- ggplot() +
geom_hline(
yintercept = 0,
color = "red",
linetype = "dashed"
) +
geom_errorbar(
data = bayes.all.slopes.age.CI,
aes(x = Age, ymin = ci_lower, ymax = ci_upper),
width = 0.2
) +
geom_point(data = bayes.all.slopes.age.CI, aes(x = Age, y = mean), size = 3) +
geom_jitter(
data = slopes,
aes(
x = Age,
y = `Bayes Percent Change`,
color = `Thermal Guild`,
fill = `Thermal Guild`,
shape = `Statistically Different`,
group = Species
),
size = 2,
width = 0.2
) +
scale_color_manual(values = thermal.guild.colors) +
scale_fill_manual(values = thermal.guild.colors) +
scale_shape_manual(values = c(19, 2)) +
labs(y = "Percent Change", x = NULL) +
theme_bw()+
theme(
#panel.background = element_rect(fill = "gray"),
#axis.text.x = element_text(angle = 45, hjust = 1, size = 12),
axis.text.y = element_text(size = 10),
axis.title.x = element_text(size = 12)
)
)
ggsave(filename = "~/GitHub/EAGER_growth/Outputs/Figures/Linear Models/bayes_all_slopes_age_percent_change.tiff",
bayes.slopes.all.age.percent.change.plot,
dpi = 300, width = 200, height = 150, units = "mm")Break adults and juveniles into their own plots
Juveniles
bayes.all.slopes.life.stage.CI <- slopes %>%
#rename(`Life Stage` = life_stage) %>%
group_by(Species, `Life Stage`) %>%
summarize(
mean = mean(`Bayes Percent Change`),
sd = sd(`Bayes Percent Change`),
n = n(),
se = sd / sqrt(n),
ci_lower = mean - qt(0.975, n - 1) * se,
ci_upper = mean + qt(0.975, n - 1) * se
)%>%
mutate(`Life Stage` = fct_rev(`Life Stage`))
(
bayes.slopes.all.plot.percent.juvenile <- ggplot(data = bayes.all.slopes.life.stage.CI %>%
filter(`Life Stage` == "Juvenile")) +
geom_hline(
yintercept = 0,
color = "red",
linetype = "dashed"
) +
geom_errorbar(
aes(
x = Species,
ymin = ci_lower,
ymax = ci_upper,
group = interaction(`Life Stage`, Species)
),
position = position_dodge(width = 0.4),
width = 0.2
) +
geom_point(
aes(
x = Species,
y = mean,
group = interaction(`Life Stage`, Species)
),
position = position_dodge(width = 0.4),
size = 3) +
geom_jitter(
data = slopes %>%
filter(`Life Stage` == "Juvenile"),
aes(
x = Species,
y = `Bayes Percent Change`,
color = `Thermal Guild`,
fill = `Thermal Guild`,
shape = `Statistically Different`,
#group = interaction(`Life Stage`, Species)
),
position = position_dodge(width = 0.4),
size = 2
) +
scale_color_manual(values = thermal.guild.colors) +
scale_fill_manual(values = thermal.guild.colors) +
scale_shape_manual(values = c(19, 2)) +
labs(y = "Percent Change",
x = NULL,
title = "Juveniles") +
theme_bw()+
theme(
#panel.background = element_rect(fill = "gray"),
,
axis.text.x = element_text(
angle = 45,
hjust = 1,
size = 12
),
axis.text.y = element_text(size = 10),
axis.title.x = element_text(size = 12)
)
)Adults
(
bayes.slopes.all.plot.percent.adult <- ggplot(data = bayes.all.slopes.life.stage.CI %>%
filter(`Life Stage` == "Adult")) +
geom_hline(
yintercept = 0,
color = "red",
linetype = "dashed"
) +
geom_errorbar(
aes(
x = Species,
ymin = ci_lower,
ymax = ci_upper,
group = interaction(`Life Stage`, Species)
),
position = position_dodge(width = 0.4),
width = 0.2
) +
geom_point(
aes(
x = Species,
y = mean,
group = interaction(`Life Stage`, Species)
),
position = position_dodge(width = 0.4),
size = 3) +
geom_jitter(
data = slopes %>%
filter(`Life Stage` == "Adult"),
aes(
x = Species,
y = `Bayes Percent Change`,
color = `Thermal Guild`,
fill = `Thermal Guild`,
shape = `Statistically Different`,
#group = interaction(`Life Stage`, Species)
),
position = position_dodge(width = 0.4),
size = 2
) +
scale_color_manual(values = thermal.guild.colors) +
scale_fill_manual(values = thermal.guild.colors) +
scale_shape_manual(values = c(19, 2)) +
labs(y = "Percent Change",
x = NULL,
title = "Adults") +
theme_bw()+
theme(
#panel.background = element_rect(fill = "gray"),
,
axis.text.x = element_text(
angle = 45,
hjust = 1,
size = 12
),
axis.text.y = element_text(size = 10),
axis.title.x = element_text(size = 12)
)
)
Model slopes w/ FTP and Life Stage
###Visualize Relationships
(
bayes.slopes.linear.plot <- ggscatter(
data = slopes,
x = "FTP",
y = "Bayes Percent Change",
color = "Life Stage",
add = "reg.line",
alpha = 0.5
) +
stat_regline_equation(aes(
label = paste(..eq.label.., ..rr.label.., sep = "~~~~"),
color = `Life Stage`
), size = 2.5, label.y.npc = "bottom") +
scale_color_viridis_d(end = 0.9, option = "C") +
labs(y = "Percent Change") +
theme(text = element_text(size = 12))
)
ggsave(
filename = "~/GitHub/EAGER_growth/Outputs/Figures/Linear Models/bayes_ftp_life_stage_slopes_raw_data.tiff",
bayes.slopes.linear.plot,
width = 200,
height = 150,
units = "mm",
dpi = 300
)
(
bayes.slopes.linear.plot.remove.spp <- ggscatter(
data = slopes %>%
filter(!Species %in% c("Rainbow Trout", "Northern Pike")),
x = "FTP",
y = "Bayes Percent Change",
color = "Life Stage",
add = "reg.line",
alpha = 0.5
) +
stat_regline_equation(aes(
label = paste(..eq.label.., ..rr.label.., sep = "~~~~"),
color = `Life Stage`
), size = 2.5, label.y.npc = "bottom") +
scale_color_viridis_d(end = 0.9, option = "C") +
labs(y = "Percent Change")+
theme(text = element_text(size = 12))
)
ggsave(
filename = "~/GitHub/EAGER_growth/Outputs/Figures/Linear Models/bayes_ftp_life_stage_slopes_raw_data_spp_removed.tiff",
bayes.slopes.linear.plot.remove.spp,
width = 200,
height = 150,
units = "mm",
dpi = 300
)
(bayes.slopes.boxplot <- ggplot(slopes, aes(x = `Life Stage`, y = slope_bayes))+
geom_point(aes(color = Species))+
geom_boxplot(outliers = F)
)
(bayes.pc.boxplot <- ggplot(slopes, aes(x = `Life Stage`, y = `Bayes Percent Change`))+
geom_point(aes(color = Species))+
geom_boxplot(outliers = F)
)
NA
NAModel
Meta-analytic model
slopes <- slopes %>%
rename(se_slope = Est.Error.begin_date_year,
life_stage = `Life Stage`)
second_model_bayes <- brm( formula = bf(
slope_bayes | se(se_slope) ~ FTP*life_stage),
data = slopes,
family = gaussian(),
chains = 4,
cores = 4,
control = list(adapt_delta = 0.8)
)
summary(second_model_bayes)Post-hoc comparisons
# 'emtrends' estimates the slope of FTP within each life_stage level
ftp_slopes <- emtrends(second_model_bayes, ~ life_stage, var = "FTP")
summary(ftp_slopes)
pairs(ftp_slopes) # Compare FTP slope between life stagesExtract posterior summary
#create range of FTP to predict over
pred_grid <- expand.grid(
FTP = seq(min(slopes$FTP, na.rm = TRUE), max(slopes$FTP, na.rm = TRUE), length.out = 100),
life_stage = unique(slopes$life_stage),
se_slope = 0
)
#extract posterior summaries and intervals for each combination of FTP and Life Stage
predictions <- add_fitted_draws(
model = second_model_bayes,
newdata = pred_grid,
re_formula = NA # Ensures population-level prediction
)
#summarize
pred_summary <- predictions %>%
group_by(FTP, life_stage) %>%
summarise(
median = median(.value),
lower = quantile(.value, 0.025),
upper = quantile(.value, 0.975)
) %>%
ungroup()Plot
Remove Rainbow Trout and Northern Pike
second_model_bayes_spp_removed <- brm( formula = bf(
slope_bayes | se(se_slope) ~ FTP*life_stage),
data = slopes %>% filter(Species %in% c("Rainbow Trout", "Northern Pike")),
family = gaussian(),
chains = 4,
cores = 4,
control = list(adapt_delta = 0.8)
)
summary(second_model_bayes_spp_removed)Post-hoc comparisons
# 'emtrends' estimates the slope of FTP within each life_stage level
ftp_slopes_spp_removed <- emtrends(second_model_bayes_spp_removed, ~ life_stage, var = "FTP")
summary(ftp_slopes_spp_removed)
pairs(ftp_slopes_spp_removed) # Compare FTP slope between life stagesExtract posterior summary
#create range of FTP to predict over
pred_grid_spp_removed <- expand.grid(
FTP = seq(min(slopes$FTP, na.rm = TRUE), max(slopes$FTP, na.rm = TRUE), length.out = 100),
life_stage = unique(slopes$life_stage),
se_slope = 0
)
#extract posterior summaries and intervals for each combination of FTP and Life Stage
predictions_spp_removed <- add_fitted_draws(
model = second_model_bayes_spp_removed,
newdata = pred_grid_spp_removed,
re_formula = NA # Ensures population-level prediction
)
#summarize
pred_summary_spp_removed <- predictions_spp_removed %>%
group_by(FTP, life_stage) %>%
summarise(
median = median(.value),
lower = quantile(.value, 0.025),
upper = quantile(.value, 0.975)
) %>%
ungroup()Plot
Fully Bayesian two-level/joint model via posterior simulation - did not end up using this approach
Multipanel figures
Figure 3
fig3.bayes <- ggarrange(
bayes.slopes.all.age.percent.change.plot,
partial.effects.life.stage.ftp.plot,
bayes.slopes.all.plot.percent.juvenile,
bayes.slopes.all.plot.percent.adult,
ncol = 2,
nrow = 3,
#common.legend = T,
legend = "right",
labels = "AUTO"
)
fig3.bayes
ggsave(
filename = "~/GitHub/EAGER_growth/Outputs/Figures/Linear Models/fig3_bayes.tiff",
fig3.bayes,
dpi = 300,
width = 300,
height = 300,
units = "mm"
)
Figure S14
figS14 <- ggarrange(
bayes.slopes.linear.plot,
bayes.slopes.linear.plot.remove.spp,
partial.effects.life.stage.ftp.plot,
partial.effects.life.stage.ftp.plot.spp.removed,
ncol = 2,
nrow = 2,
common.legend = T,
legend = "right",
labels = "AUTO",
hjust = -1.4
)
figS14
ggsave(
filename = "~/GitHub/EAGER_growth/Outputs/Figures/Linear Models/figS14.tiff",
figS14,
dpi = 300,
width = 200,
height = 150,
units = "mm"
)
Post-2015
Aging protocols changed after 2015 to include multiple structures for some species. We are investigating if there is any evidence that trends change post-2015.
Plot raw data
grow.merge.target.spp.clean <- grow.merge.target.spp %>%
mutate(species = species %>%
str_replace_all("_", " ") %>%
str_to_title()) %>%
rename(`Age Group` = age_group)
(all.spp.year <- ggplot(data = grow.merge.target.spp.clean,
aes(x = begin_date_year, y = length_mean_mm,
color = `Age Group`, fill = `Age Group`))+
#geom_point(alpha = 0.1)+
geom_smooth()+
geom_vline(xintercept = 2015, color = "red", linetype = "dashed")+
facet_wrap(~species, scales = "free")+
scale_y_continuous(expand = c(0,0))+
scale_color_viridis_d()+
scale_fill_viridis_d()+
theme_bw()
)
#No obvious differences in trends post-2015
ggsave(
filename = "~/GitHub/EAGER_growth/Outputs/Figures/raw_data_smooth_spp_age_group.tiff",
all.spp.year,
dpi = 300,
width = 300,
height = 250,
units = "mm"
)---
title: "03_linear_models"
author: "Peter Flood"
date: "2024-09-03"
output: 
  html_document:
    toc: yes
    df_print: paged
  html_notebook:
    toc: yes
    toc_float: yes

---
```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE, warning = FALSE, message = FALSE)
getwd()
```

# Install Libraries
```{r}
#Install libraries if needed

#Need to install one from GitHub

if (!require("tidyverse")) install.packages("tidyverse")
if (!require("openxlsx")) install.packages("openxlsx")
if (!require("RVAideMemoire")) install.packages("RVAideMemoire")
if (!require("DHARMa")) install.packages("DHARMa")
if (!require("fitdistrplus")) install.packages("fitdistrplus")
if (!require("logspline")) install.packages("logspline")
if (!require("car")) install.packages("car")
if (!require("effectsize")) install.packages("effectsize")
if (!require("ggeffects")) install.packages("ggeffects")
if (!require("marginaleffects")) install.packages("marginaleffects")
if (!require("emmeans")) install.packages("emmeans")
if (!require("ggpmisc")) install.packages("ggpmisc")
if (!require("ggpubr")) install.packages("ggpubr")
if (!require("ggrepel")) install.packages("ggrepel")
if (!require("rstatix")) install.packages("rstatix")
if (!require("brms")) install.packages("brms")
if (!require("loo")) install.packages("loo")
if (!require("MuMIn")) install.packages("MuMIn")
if (!require("tidybayes")) install.packages("tidybayes")


```


# Load Libraries
```{r}

library(openxlsx) #read and write .xlsx
library(RVAideMemoire) #used for testing lm model assumptions
library(DHARMa) #checking model assumptions and output
library(fitdistrplus) #insight into which distribution to use
library(logspline) #k-s test for selected distribution 
library(car) #partial regression plots
library(effectsize) #calculate effect sizes
library(ggeffects) #marginal effect plots
library(marginaleffects) #marginal effects
library(emmeans) #posthoc analyses
library(ggpmisc) 
library(ggpubr) #arranging multipanel plots
library(ggrepel) #text labels on plots w/o overlapping
library(rstatix) #used for emmeans wrappers 
library(brms) #Bayesian modeling
library(loo) #leave-one-out cross-validation of Bayesian models
library(MuMIn) #multi-model selection
library(tidyverse) #data manipulation
library(tidybayes) #data manipulation

```

# Load Data
```{r}
all.grow.merge <- read.xlsx("~/GitHub/EAGER_growth/Outputs/all_grow_lake_join.xlsx") %>% 
  mutate(age_group = as.factor(age_group))
```

## Format data
Mean length per species per age class over the entire dataset
```{r}
mean.length.age.class <- all.grow.merge %>% 
  group_by(species, age_group) %>% 
  summarise(mean = mean(length_mean_mm, na.rm = T)) %>% 
  mutate(Species = case_when(
    species == "black_crappie" ~ "Black Crappie",
    species == "bluegill" ~ "Bluegill",
    species == "brown_trout" ~ "Brown Trout",
    species == "cisco" ~ "Cisco",
    species == "common_white_sucker" ~ "White Sucker",
    species == "largemouth_bass" ~ "Largemouth Bass",
    species == "northern_pike" ~ "Northern Pike",
    species == "pumpkinseed_sunfish" ~ "Pumpkinseed Sunfish",
    species == "rainbow_trout" ~ "Rainbow Trout",
    species == "rock_bass" ~ "Rock Bass", 
    species == "smallmouth_bass" ~ "Smallmouth Bass",
    species == "walleye" ~ "Walleye",
    species == "yellow_perch" ~ "Yellow Perch",
    T ~ species
  )) %>% 
  rename(Age = age_group) 
```

# Linear Regressions
Data may not conform well to model assumptions. The goal of these models is to ask whether or not a given species size class is increasing or decreasing in size through time. Not necessary to over interpret the exact amount of change, although we do report that, it should be taken with a grain of salt.

## Black Crappie

###Model
```{r}
#filter data for only Black Crappie, Pomooxis nigromaculatus 
pomnig <- all.grow.merge %>% filter(species == "black_crappie") %>% 
  filter(!age_group %in% c(12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22), #match age groups in BRTs
         !is.na(age_group), !is.na(begin_date_year), !is.na(length_mean_mm),
         !is.na(log_max_depth), !is.na(logarea)) %>% 
  mutate(age_group = fct_rev(age_group))

#linear model
pomnig.lm <- lm(length_mean_mm ~ begin_date_year*age_group + log_max_depth + logarea + doy, data = pomnig)
summary(pomnig.lm)

#calculate and interpret effect sizes
eta_squared(pomnig.lm)
#interpret(eta_squared(pomnig.lm), rules = "cohen1992")

#calculate AIC score
AIC(pomnig.lm)

#examine model fit
testDispersion(pomnig.lm)

simulation.output <- simulateResiduals(fittedModel = pomnig.lm)

residuals(pomnig.lm)
residuals(pomnig.lm, quantileFunction = qnorm)

plot(pomnig.lm)
```

###Post-hoc comparisons
```{r}
#post-hoc comparisons
pomnig.emm <- emmeans(pomnig.lm, ~ begin_date_year*age_group)
pairs(pomnig.emm, simple = "age_group")
test(pairs(pomnig.emm, by = "begin_date_year"), by = NULL, adjust = "mvt")

#export tables
# interpret(eta_squared(pomnig.lm), rules = "cohen1992") %>%
#   write.csv(file = "Outputs/Tables/pomnig_eta_squared.csv")

#slope contrasts
# Obtain slope estimates
pomnig.slopes <- emtrends(pomnig.lm, ~age_group, var = "begin_date_year")
# Compare estimated slopes to 0
pomnig.slope.contrasts <- test(pomnig.slopes) %>% 
  mutate(Species = "Black Crappie") %>% 
  rename(Age = age_group)

pomnig.slope.contrasts %>%
  write.csv(file = "Outputs/Tables/pomnig_emmeans.csv")
```

### Plot raw data
```{r}
(pomnig.length.year.plot <- ggplot(data = pomnig %>% 
                              mutate(Age = as.factor(age_group)), 
                            aes(x = begin_date_year, y = length_mean_mm, 
                                color = Age, fill = Age, group = Age))+
    #geom_point()+
    geom_smooth(method = "lm")+
    stat_poly_eq(use_label("eq"))+
    scale_color_viridis_d()+
    scale_fill_viridis_d()+
    labs(y = "Length (mm)", x = "Year")+
    theme_bw()
)

```
### Marginal Effects
```{r}
result <- predict_response(pomnig.lm, c("begin_date_year", "age_group"))

# plot(result)
# test_predictions(result) %>% 
#   write.csv(file = "Outputs/Tables/pomnig_pairwise_length_time_slopes.csv", row.names = F)

(pomnig.marginal.plot <- ggpredict(pomnig.lm, terms = c("begin_date_year", "age_group")) %>%  
    plot()+
    #stat_poly_eq(use_label("eq"))+
    stat_regline_equation()+
    stat_poly_line()+
    # annotate(geom = "text", label = "y = 1000 - 0.35x", x = 2000, y = 350)+
    # annotate(geom = "text", label = "y = 21 + 0.093x", x = 2000, y = 212)+
    # annotate(geom = "text", label = "y = -64 + 0.13x", x = 2000, y = 199)+
    # annotate(geom = "text", label = "y = 230 - 0.028x", x = 2000, y = 182)+
    # annotate(geom = "text", label = "y = 610 - 0.23x", x = 2000, y = 160)+
    # annotate(geom = "text", label = "y = 920 - 0.39x", x = 2000, y = 137)+
    # annotate(geom = "text", label = "y = 1200 - 0.55x", x = 2000, y = 110)+
    # annotate(geom = "text", label = "y = 1700 - 0.83x", x = 2000, y = 80)+
    scale_color_viridis_d()+
    scale_fill_viridis_d()+
    labs(title = NULL,
         y = "Mean Length-at-Age (mm)",
         x = "Year")+
    guides(fill = guide_legend(title = "Age", reverse = T), color = guide_legend(title = "Age", reverse = T))+
    theme_bw()
)


ggsave(filename = "~/GitHub/EAGER_growth/Outputs/Figures/Linear Models/pomnig_marginal_effects_plot.tiff", 
       pomnig.marginal.plot, 
       dpi = 300, width = 200, height = 150, units = "mm")
```


## Bluegill

### Model
```{r}
#filter data for only Black Crappie, Pomooxis nigromaculatus 
lepmac <- all.grow.merge %>% filter(species == "bluegill") %>% 
  filter(!age_group %in% c(12, 13, 18), #match age groups in BRTs
         !is.na(age_group), !is.na(begin_date_year), !is.na(length_mean_mm),
         !is.na(log_max_depth), !is.na(logarea))

#linear model
lepmac.lm <- lm(length_mean_mm ~ begin_date_year*age_group + log_max_depth + logarea + doy, data = lepmac)
summary(lepmac.lm)

#calculate and interpret effect sizes
eta_squared(lepmac.lm)
#interpret(eta_squared(lepmac.lm), rules = "cohen1992")

#calculate AIC score
AIC(lepmac.lm)

#examine model fit
testDispersion(lepmac.lm)

simulation.output <- simulateResiduals(fittedModel = lepmac.lm)

residuals(lepmac.lm)
residuals(lepmac.lm, quantileFunction = qnorm)

plot(lepmac.lm)
```

### Post-hoc comparisons
```{r}
lepmac.emm <- emmeans(lepmac.lm, ~ begin_date_year*age_group)
pairs(lepmac.emm, simple = "age_group")
test(pairs(lepmac.emm, by = "begin_date_year"), by = NULL, adjust = "mvt")

#export tables
# #interpret(eta_squared(lepmac.lm), rules = "cohen1992") %>%
#   write.csv(file = "Outputs/Tables/lepmac_eta_squared.csv")

#slope contrasts
# Obtain slope estimates
lepmac.slopes <- emtrends(lepmac.lm, ~age_group, var = "begin_date_year")
# Compare estimated slopes to 0
lepmac.slope.contrasts <- test(lepmac.slopes) %>% 
  mutate(Species = "Bluegill") %>% 
  rename(Age = age_group)

lepmac.slope.contrasts %>%
  write.csv(file = "Outputs/Tables/lepmac_emmeans.csv")
```

### Plot raw data
```{r}
(lepmac.length.year.plot <- ggplot(data = lepmac %>% 
                              mutate(Age = as.factor(age_group)), 
                            aes(x = begin_date_year, y = length_mean_mm, 
                                color = Age, fill = Age, group = Age))+
    #geom_point()+
    geom_smooth(method = "lm")+
    stat_poly_eq(use_label("eq"))+
    scale_color_viridis_d()+
    scale_fill_viridis_d()+
    labs(y = "Length (mm)", x = "Year")+
    theme_bw()
)

```
### Marginal Effects
```{r}
result <- predict_response(lepmac.lm, c("begin_date_year", "age_group"))

# plot(result)
# test_predictions(result) %>% 
#   write.csv(file = "Outputs/Tables/lepmac_pairwise_length_time_slopes.csv", row.names = F)

(lepmac.marginal.plot <- ggpredict(lepmac.lm, terms = c("begin_date_year", "age_group")) %>%  
    plot()+
    #stat_poly_eq(use_label("eq"))+
    stat_regline_equation()+
    stat_poly_line()+
    # annotate(geom = "text", label = "y = 77 + 0.07x", x = 2000, y = 224)+
    # annotate(geom = "text", label = "y = 21 + 0.093x", x = 2000, y = 212)+
    # annotate(geom = "text", label = "y = -64 + 0.13x", x = 2000, y = 199)+
    # annotate(geom = "text", label = "y = 230 - 0.028x", x = 2000, y = 182)+
    # annotate(geom = "text", label = "y = 610 - 0.23x", x = 2000, y = 160)+
    # annotate(geom = "text", label = "y = 920 - 0.39x", x = 2000, y = 137)+
    # annotate(geom = "text", label = "y = 1200 - 0.55x", x = 2000, y = 110)+
    # annotate(geom = "text", label = "y = 1700 - 0.83x", x = 2000, y = 80)+
    scale_color_viridis_d()+
    scale_fill_viridis_d()+
    labs(title = NULL,
         y = "Mean Length-at-Age (mm)",
         x = "Year")+
    guides(fill = guide_legend(title = "Age", reverse = T), color = guide_legend(title = "Age", reverse = T))+
    theme_bw()
)


ggsave(filename = "~/GitHub/EAGER_growth/Outputs/Figures/Linear Models/lepmac_marginal_effects_plot.tiff", 
       lepmac.marginal.plot, 
       dpi = 300, width = 200, height = 150, units = "mm")
```

## Brown Trout

### Model
```{r}
#filter data for only Black Crappie, Pomooxis nigromaculatus 
saltru <- all.grow.merge %>% filter(species == "brown_trout") %>% 
  filter(age_group %in% c(1, 2, 3, 4, 5), #match age groups in BRTs
         !is.na(age_group), !is.na(begin_date_year), !is.na(length_mean_mm),
         !is.na(log_max_depth), !is.na(logarea))

#linear model
saltru.lm <- lm(length_mean_mm ~ begin_date_year*age_group + log_max_depth + logarea + doy, data = saltru)
summary(saltru.lm)

#calculate and interpret effect sizes
eta_squared(saltru.lm)
#interpret(eta_squared(saltru.lm), rules = "cohen1992")

#calculate AIC score
AIC(saltru.lm)

#examine model fit
testDispersion(saltru.lm)

simulation.output <- simulateResiduals(fittedModel = saltru.lm)

residuals(saltru.lm)
residuals(saltru.lm, quantileFunction = qnorm)

plot(saltru.lm)
```

### Post-hoc comparisons
```{r}
saltru.emm <- emmeans(saltru.lm, ~ begin_date_year*age_group)
pairs(saltru.emm, simple = "age_group")
test(pairs(saltru.emm, by = "begin_date_year"), by = NULL, adjust = "mvt")

#export tables
# #interpret(eta_squared(saltru.lm), rules = "cohen1992") %>%
#   write.csv(file = "Outputs/Tables/saltru_eta_squared.csv")

#slope contrasts
# Obtain slope estimates
saltru.slopes <- emtrends(saltru.lm, ~age_group, var = "begin_date_year")
# Compare estimated slopes to 0
saltru.slope.contrasts <- test(saltru.slopes) %>% 
  mutate(Species = "Brown Trout") %>% 
  rename(Age = age_group)

saltru.slope.contrasts %>%
  write.csv(file = "Outputs/Tables/saltru_emmeans.csv")
```

### Plot raw data
```{r}
(saltru.length.year.plot <- ggplot(data = saltru %>% 
                              mutate(Age = as.factor(age_group)), 
                            aes(x = begin_date_year, y = length_mean_mm, 
                                color = Age, fill = Age, group = Age))+
    #geom_point()+
    geom_smooth(method = "lm")+
    stat_poly_eq(use_label("eq"))+
    scale_color_viridis_d()+
    scale_fill_viridis_d()+
    labs(y = "Length (mm)", x = "Year")+
    theme_bw()
)

```

### Marginal Effects
```{r}
result <- predict_response(saltru.lm, c("begin_date_year", "age_group"))

# plot(result)
# test_predictions(result) %>% 
#   write.csv(file = "Outputs/Tables/saltru_pairwise_length_time_slopes.csv", row.names = F)

(saltru.marginal.plot <- ggpredict(saltru.lm, terms = c("begin_date_year", "age_group")) %>%  
    plot()+
    #stat_poly_eq(use_label("eq"))+
    stat_regline_equation()+
    stat_poly_line()+
    # annotate(geom = "text", label = "y = 3000 - 1.2x", x = 2000, y = 250)+
    # annotate(geom = "text", label = "y = 2000 - 0.76x", x = 2000, y = 350)+
    # annotate(geom = "text", label = "y = 1900 - 0.76x", x = 2000, y = 430)+
    # annotate(geom = "text", label = "y = 3200 - 1.4x", x = 2000, y = 500)+
    # annotate(geom = "text", label = "y = 4100 - 1.9x", x = 2000, y = 550)+
    scale_color_viridis_d()+
    scale_fill_viridis_d()+
    labs(title = NULL,
         y = "Mean Length-at-Age (mm)",
         x = "Year")+
    guides(fill = guide_legend(title = "Age", reverse = T), color = guide_legend(title = "Age", reverse = T))+
    theme_bw()
)


ggsave(filename = "~/GitHub/EAGER_growth/Outputs/Figures/Linear Models/saltru_marginal_effects_plot.tiff", 
       saltru.marginal.plot, 
       dpi = 300, width = 200, height = 150, units = "mm")
```




## Cisco

### Model
```{r}
#filter data for only Black Crappie, Pomooxis nigromaculatus 
corart <- all.grow.merge %>% filter(species == "cisco") %>% 
  filter(age_group %in% c(2, 3, 4, 5, 6, 7), #match age groups in BRTs
         !is.na(age_group), !is.na(begin_date_year), !is.na(length_mean_mm),
         !is.na(log_max_depth), !is.na(logarea))

#linear model
corart.lm <- lm(length_mean_mm ~ begin_date_year*age_group + log_max_depth + logarea + doy, data = corart)
summary(corart.lm)

#calculate and interpret effect sizes
eta_squared(corart.lm)
#interpret(eta_squared(corart.lm), rules = "cohen1992")

#calculate AIC score
AIC(corart.lm)

#examine model fit
testDispersion(corart.lm)

simulation.output <- simulateResiduals(fittedModel = corart.lm)

residuals(corart.lm)
residuals(corart.lm, quantileFunction = qnorm)

plot(corart.lm)
```

### Post-hoc comparisons
```{r}
corart.emm <- emmeans(corart.lm, ~ begin_date_year*age_group)
pairs(corart.emm, simple = "age_group")
test(pairs(corart.emm, by = "begin_date_year"), by = NULL, adjust = "mvt")

#export tables
# #interpret(eta_squared(corart.lm), rules = "cohen1992") %>%
#   write.csv(file = "Outputs/Tables/corart_eta_squared.csv")

#slope contrasts
# Obtain slope estimates
corart.slopes <- emtrends(corart.lm, ~age_group, var = "begin_date_year")
# Compare estimated slopes to 0
corart.slope.contrasts <- test(corart.slopes) %>% 
  mutate(Species = "Cisco") %>% 
  rename(Age = age_group)

corart.slope.contrasts %>%
  write.csv(file = "Outputs/Tables/corart_emmeans.csv")
```

### Plot raw data
```{r}
(corart.length.year.plot <- ggplot(data = corart %>% 
                              mutate(Age = as.factor(age_group)), 
                            aes(x = begin_date_year, y = length_mean_mm, 
                                color = Age, fill = Age, group = Age))+
    #geom_point()+
    geom_smooth(method = "lm")+
    stat_poly_eq(use_label("eq"))+
    scale_color_viridis_d()+
    scale_fill_viridis_d()+
    labs(y = "Length (mm)", x = "Year")+
    theme_bw()
)

```

### Marginal Effects
```{r}
result <- predict_response(corart.lm, c("begin_date_year", "age_group"))

# plot(result)
# test_predictions(result) %>% 
#   write.csv(file = "Outputs/Tables/corart_pairwise_length_time_slopes.csv", row.names = F)

(corart.marginal.plot <- ggpredict(corart.lm, terms = c("begin_date_year", "age_group")) %>%  
    plot()+
    #stat_poly_eq(use_label("eq"))+
    stat_regline_equation()+
    stat_poly_line()+
    # annotate(geom = "text", label = "y = 1100 + 0.39x", x = 1970, y = 400)+
    # annotate(geom = "text", label = "y = 340 + 0.01x", x = 1970, y = 375)+
    # annotate(geom = "text", label = "y = -740 + 0.55x", x = 1970, y = 350)+
    # annotate(geom = "text", label = "y = 1400 - 0.57x", x = 1980, y = 325)+
    # annotate(geom = "text", label = "y = 860 - 0.29x", x = 1970, y = 310)+
    # annotate(geom = "text", label = "y = 1700 - 0.75x", x = 1970, y = 275)+
    scale_color_viridis_d()+
    scale_fill_viridis_d()+
    labs(title = NULL,
         y = "Mean Length-at-Age (mm)",
         x = "Year")+
    guides(fill = guide_legend(title = "Age", reverse = T), color = guide_legend(title = "Age", reverse = T))+
    theme_bw()
)


ggsave(filename = "~/GitHub/EAGER_growth/Outputs/Figures/Linear Models/corart_marginal_effects_plot.tiff", 
       corart.marginal.plot, 
       dpi = 300, width = 200, height = 150, units = "mm")
```




## White Sucker

### Model
```{r}
#filter data for only Black Crappie, Pomooxis nigromaculatus 
catcom <- all.grow.merge %>% filter(species == "common_white_sucker") %>% 
  filter(age_group %in% c(1, 2, 3, 4, 5), #match age groups in BRTs
         !is.na(age_group), !is.na(begin_date_year), !is.na(length_mean_mm),
         !is.na(log_max_depth), !is.na(logarea))

#linear model
catcom.lm <- lm(length_mean_mm ~ begin_date_year*age_group + log_max_depth + logarea + doy, data = catcom)
summary(catcom.lm)

#calculate and interpret effect sizes
eta_squared(catcom.lm)
#interpret(eta_squared(catcom.lm), rules = "cohen1992")

#calculate AIC score
AIC(catcom.lm)

#examine model fit
testDispersion(catcom.lm)

simulation.output <- simulateResiduals(fittedModel = catcom.lm)

residuals(catcom.lm)
residuals(catcom.lm, quantileFunction = qnorm)

plot(catcom.lm)
```

### Post-hoc comparisons
```{r}
catcom.emm <- emmeans(catcom.lm, ~ begin_date_year*age_group)
pairs(catcom.emm, simple = "age_group")
test(pairs(catcom.emm, by = "begin_date_year"), by = NULL, adjust = "mvt")

#export tables
# #interpret(eta_squared(catcom.lm), rules = "cohen1992") %>%
#   write.csv(file = "Outputs/Tables/catcom_eta_squared.csv")

#slope contrasts
# Obtain slope estimates
catcom.slopes <- emtrends(catcom.lm, ~age_group, var = "begin_date_year")
# Compare estimated slopes to 0
catcom.slope.contrasts <- test(catcom.slopes) %>% 
  mutate(Species = "White Sucker") %>% 
  rename(Age = age_group)

catcom.slope.contrasts %>%
  write.csv(file = "Outputs/Tables/catcom_emmeans.csv")
```

### Plot raw data
```{r}
(catcom.length.year.plot <- ggplot(data = catcom %>% 
                              mutate(Age = as.factor(age_group)), 
                            aes(x = begin_date_year, y = length_mean_mm, 
                                color = Age, fill = Age, group = Age))+
    #geom_point()+
    geom_smooth(method = "lm")+
    stat_poly_eq(use_label("eq"))+
    scale_color_viridis_d()+
    scale_fill_viridis_d()+
    labs(y = "Length (mm)", x = "Year")+
    theme_bw()
)

```
### Marginal Effects
```{r}
result <- predict_response(catcom.lm, c("begin_date_year", "age_group"))

# plot(result)
# test_predictions(result) %>% 
#   write.csv(file = "Outputs/Tables/catcom_pairwise_length_time_slopes.csv", row.names = F)

(catcom.marginal.plot <- ggpredict(catcom.lm, terms = c("begin_date_year", "age_group")) %>%  
    plot()+
    #stat_poly_eq(use_label("eq"))+
    stat_regline_equation()+
    stat_poly_line()+
    # annotate(geom = "text", label = "y = 2400 - 0.99x", x = 2000, y = 450)+
    # annotate(geom = "text", label = "y = 4300 - 2x", x = 2000, y = 400)+
    # annotate(geom = "text", label = "y = 4300 - 2x", x = 2000, y = 350)+
    # annotate(geom = "text", label = "y = 3700 - 1.7x", x = 2000, y = 300)+
    # annotate(geom = "text", label = "y = 2600 - 1.2x", x = 2000, y = 200)+
    scale_color_viridis_d()+
    scale_fill_viridis_d()+
    labs(title = NULL,
         y = "Mean Length-at-Age (mm)",
         x = "Year")+
    guides(fill = guide_legend(title = "Age", reverse = T), color = guide_legend(title = "Age", reverse = T))+
    theme_bw()
)


ggsave(filename = "~/GitHub/EAGER_growth/Outputs/Figures/Linear Models/catcom_marginal_effects_plot.tiff", 
       catcom.marginal.plot, 
       dpi = 300, width = 200, height = 150, units = "mm")
```




## Largemouth Bass

### Model
```{r}
#filter data for only Black Crappie, Pomooxis nigromaculatus 
micsal <- all.grow.merge %>% filter(species == "largemouth_bass") %>% 
  filter(!age_group %in% c(13, 14, 15, 16, 17, 18, 19, 20, 21, 22), #match age groups in BRTs
         !is.na(age_group), !is.na(begin_date_year), !is.na(length_mean_mm),
         !is.na(log_max_depth), !is.na(logarea))

#linear model
micsal.lm <- lm(length_mean_mm ~ begin_date_year*age_group + log_max_depth + logarea + doy, data = micsal)
summary(micsal.lm)

#calculate and interpret effect sizes
eta_squared(micsal.lm)
#interpret(eta_squared(micsal.lm), rules = "cohen1992")

#calculate AIC score
AIC(micsal.lm)

#examine model fit
testDispersion(micsal.lm)

simulation.output <- simulateResiduals(fittedModel = micsal.lm)

residuals(micsal.lm)
residuals(micsal.lm, quantileFunction = qnorm)

plot(micsal.lm)
```

### Post-hoc comparisons
```{r}
micsal.emm <- emmeans(micsal.lm, ~ begin_date_year*age_group)
pairs(micsal.emm, simple = "age_group")
test(pairs(micsal.emm, by = "begin_date_year"), by = NULL, adjust = "mvt")

#export tables
# #interpret(eta_squared(micsal.lm), rules = "cohen1992") %>%
#   write.csv(file = "Outputs/Tables/micsal_eta_squared.csv")

#slope contrasts
# Obtain slope estimates
micsal.slopes <- emtrends(micsal.lm, ~age_group, var = "begin_date_year")
# Compare estimated slopes to 0
micsal.slope.contrasts <- test(micsal.slopes) %>% 
  mutate(Species = "Largemouth Bass") %>% 
  rename(Age = age_group)

micsal.slope.contrasts %>%
  write.csv(file = "Outputs/Tables/micsal_emmeans.csv")
```

### Plot raw data
```{r}
(micsal.length.year.plot <- ggplot(data = micsal %>% 
                              mutate(Age = as.factor(age_group)), 
                            aes(x = begin_date_year, y = length_mean_mm, 
                                color = Age, fill = Age, group = Age))+
    #geom_point()+
    geom_smooth(method = "lm")+
    stat_poly_eq(use_label("eq"))+
    scale_color_viridis_d()+
    scale_fill_viridis_d()+
    labs(y = "Length (mm)", x = "Year")+
    theme_bw()
)

```
### Marginal Effects
```{r}
result <- predict_response(micsal.lm, c("begin_date_year", "age_group"))

# plot(result)
# test_predictions(result) %>% 
#   write.csv(file = "Outputs/Tables/micsal_pairwise_length_time_slopes.csv", row.names = F)

(micsal.marginal.plot <- ggpredict(micsal.lm, terms = c("begin_date_year", "age_group")) %>%  
    plot()+
    #stat_poly_eq(use_label("eq"))+
    stat_regline_equation()+
    stat_poly_line()+
    # annotate(geom = "text", label = "y = 77 + 0.07x", x = 2000, y = 224)+
    # annotate(geom = "text", label = "y = 21 + 0.093x", x = 2000, y = 212)+
    # annotate(geom = "text", label = "y = -64 + 0.13x", x = 2000, y = 199)+
    # annotate(geom = "text", label = "y = 230 - 0.028x", x = 2000, y = 182)+
    # annotate(geom = "text", label = "y = 610 - 0.23x", x = 2000, y = 160)+
    # annotate(geom = "text", label = "y = 920 - 0.39x", x = 2000, y = 137)+
    # annotate(geom = "text", label = "y = 1200 - 0.55x", x = 2000, y = 110)+
    # annotate(geom = "text", label = "y = 1700 - 0.83x", x = 2000, y = 80)+
    scale_color_viridis_d()+
    scale_fill_viridis_d()+
    labs(title = NULL,
         y = "Mean Length-at-Age (mm)",
         x = "Year")+
    guides(fill = guide_legend(title = "Age", reverse = T), color = guide_legend(title = "Age", reverse = T))+
    theme_bw()
)


ggsave(filename = "~/GitHub/EAGER_growth/Outputs/Figures/Linear Models/micsal_marginal_effects_plot.tiff", 
       micsal.marginal.plot, 
       dpi = 300, width = 200, height = 150, units = "mm")
```




## Northern Pike

### Model
```{r}
#filter data for only Black Crappie, Pomooxis nigromaculatus 
esoluc <- all.grow.merge %>% filter(species == "northern_pike") %>% 
  filter(!age_group %in% c(11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22), #match age groups in BRTs
         !is.na(age_group), !is.na(begin_date_year), !is.na(length_mean_mm),
         !is.na(log_max_depth), !is.na(logarea))

#linear model
esoluc.lm <- lm(length_mean_mm ~ begin_date_year*age_group + log_max_depth + logarea + doy, data = esoluc)
summary(esoluc.lm)

#calculate and interpret effect sizes
eta_squared(esoluc.lm)
#interpret(eta_squared(esoluc.lm), rules = "cohen1992")

#calculate AIC score
AIC(esoluc.lm)

#examine model fit
testDispersion(esoluc.lm)

simulation.output <- simulateResiduals(fittedModel = esoluc.lm)

residuals(esoluc.lm)
residuals(esoluc.lm, quantileFunction = qnorm)

plot(esoluc.lm)
```

### Post-hoc comparisons
```{r}
esoluc.emm <- emmeans(esoluc.lm, ~ begin_date_year*age_group)
pairs(esoluc.emm, simple = "age_group")
test(pairs(esoluc.emm, by = "begin_date_year"), by = NULL, adjust = "mvt")

#export tables
# #interpret(eta_squared(esoluc.lm), rules = "cohen1992") %>%
#   write.csv(file = "Outputs/Tables/esoluc_eta_squared.csv")

#slope contrasts
# Obtain slope estimates
esoluc.slopes <- emtrends(esoluc.lm, ~age_group, var = "begin_date_year")
# Compare estimated slopes to 0
esoluc.slope.contrasts <- test(esoluc.slopes) %>% 
  mutate(Species = "Northern Pike") %>% 
  rename(Age = age_group)

esoluc.slope.contrasts %>%
  write.csv(file = "Outputs/Tables/esoluc_emmeans.csv")
```

### Plot raw data
```{r}
(esoluc.length.year.plot <- ggplot(data = esoluc %>% 
                              mutate(Age = as.factor(age_group)), 
                            aes(x = begin_date_year, y = length_mean_mm, 
                                color = Age, fill = Age, group = Age))+
    #geom_point()+
    geom_smooth(method = "lm")+
    stat_poly_eq(use_label("eq"))+
    scale_color_viridis_d()+
    scale_fill_viridis_d()+
    labs(y = "Length (mm)", x = "Year")+
    theme_bw()
)

```
### Marginal Effects
```{r}
result <- predict_response(esoluc.lm, c("begin_date_year", "age_group"))

# plot(result)
# test_predictions(result) %>% 
#   write.csv(file = "Outputs/Tables/esoluc_pairwise_length_time_slopes.csv", row.names = F)

(esoluc.marginal.plot <- ggpredict(esoluc.lm, terms = c("begin_date_year", "age_group")) %>%  
    plot()+
    #stat_poly_eq(use_label("eq"))+
    stat_regline_equation()+
    stat_poly_line()+
    # annotate(geom = "text", label = "y = 1.3E4 - 5.5x", x = 1950, y = 1300)+
    # annotate(geom = "text", label = "y = 9.1E3 - 4.1x", x = 1950, y = 1150)+
    # annotate(geom = "text", label = "y = 8.9E3 - 4.1x", x = 1950, y = 1050)+
    # annotate(geom = "text", label = "y = 6.5E3 - 2.9x", x = 1950, y = 925)+
    # annotate(geom = "text", label = "y = 5.2E3 - 2.2x", x = 1950, y = 850)+
    # annotate(geom = "text", label = "y = 3.4E3 - 1.4x", x = 1950, y = 750)+
    # annotate(geom = "text", label = "y = 2.6E3 - 1x", x = 1950, y = 680)+
    # annotate(geom = "text", label = "y = 1.8E3 - 0.64x", x = 1950, y = 615)+
    # annotate(geom = "text", label = "y = 2.4E3 - 0.99x", x = 1950, y = 550)+
    # annotate(geom = "text", label = "y = 3.2E3 - 1.4x", x = 1950, y = 460)+
    # annotate(geom = "text", label = "y = 4.3E3 - 2.1x", x = 1950, y = 375)+
    scale_color_viridis_d()+
    scale_fill_viridis_d()+
    labs(title = NULL,
         y = "Mean Length-at-Age (mm)",
         x = "Year")+
    guides(fill = guide_legend(title = "Age", reverse = T), color = guide_legend(title = "Age", reverse = T))+
    theme_bw()
)


ggsave(filename = "~/GitHub/EAGER_growth/Outputs/Figures/Linear Models/esoluc_marginal_effects_plot.tiff", 
       esoluc.marginal.plot, 
       dpi = 300, width = 200, height = 150, units = "mm")
```

## Pumpkinseed Sunfish

### Model
```{r}
#filter data for only Black Crappie, Pomooxis nigromaculatus 
lepgib <- all.grow.merge %>% filter(species == "pumpkinseed_sunfish") %>% 
  filter(age_group %in% c(1, 2, 3, 4, 5, 6, 7, 8, 9, 10), #match age groups in BRTs
         !is.na(age_group), !is.na(begin_date_year), !is.na(length_mean_mm),
         !is.na(log_max_depth), !is.na(logarea))

#linear model
lepgib.lm <- lm(length_mean_mm ~ begin_date_year*age_group + log_max_depth + logarea + doy, data = lepgib)
summary(lepgib.lm)

#calculate and interpret effect sizes
eta_squared(lepgib.lm)
#interpret(eta_squared(lepgib.lm), rules = "cohen1992")

#calculate AIC score
AIC(lepgib.lm)

#examine model fit
testDispersion(lepgib.lm)

simulation.output <- simulateResiduals(fittedModel = lepgib.lm)

residuals(lepgib.lm)
residuals(lepgib.lm, quantileFunction = qnorm)

plot(lepgib.lm)
```

### Post-hoc comparisons
```{r}
lepgib.emm <- emmeans(lepgib.lm, ~ begin_date_year*age_group)
pairs(lepgib.emm, simple = "age_group")
test(pairs(lepgib.emm, by = "begin_date_year"), by = NULL, adjust = "mvt")

#export tables
# #interpret(eta_squared(lepgib.lm), rules = "cohen1992") %>%
#   write.csv(file = "Outputs/Tables/lepgib_eta_squared.csv")

#slope contrasts
# Obtain slope estimates
lepgib.slopes <- emtrends(lepgib.lm, ~age_group, var = "begin_date_year")
# Compare estimated slopes to 0
lepgib.slope.contrasts <- test(lepgib.slopes) %>% 
  mutate(Species = "Pumpkinseed Sunfish") %>% 
  rename(Age = age_group)

lepgib.slope.contrasts %>%
  write.csv(file = "Outputs/Tables/lepgib_emmeans.csv")
```

### Plot raw data
```{r}
(lepgib.length.year.plot <- ggplot(data = lepgib %>% 
                              mutate(Age = as.factor(age_group)), 
                            aes(x = begin_date_year, y = length_mean_mm, 
                                color = Age, fill = Age, group = Age))+
    #geom_point()+
    geom_smooth(method = "lm")+
    stat_poly_eq(use_label("eq"))+
    scale_color_viridis_d()+
    scale_fill_viridis_d()+
    labs(y = "Length (mm)", x = "Year")+
    theme_bw()
)

```
### Marginal Effects
```{r}
result <- predict_response(lepgib.lm, c("begin_date_year", "age_group"))

# plot(result)
# test_predictions(result) %>% 
#   write.csv(file = "Outputs/Tables/lepgib_pairwise_length_time_slopes.csv", row.names = F)

(lepgib.marginal.plot <- ggpredict(lepgib.lm, terms = c("begin_date_year", "age_group")) %>%  
    plot()+
    #stat_poly_eq(use_label("eq"))+
    stat_regline_equation()+
    stat_poly_line()+
    # annotate(geom = "text", label = "y = -220 + 0.22x", x = 1990, y = 220)+
    # annotate(geom = "text", label = "y = -1.7 + 0.11x", x = 1990, y = 213)+
    # annotate(geom = "text", label = "y = -64 + 0.13x", x = 1990, y = 205)+
    # annotate(geom = "text", label = "y = 300 + 0.25x", x = 1990, y = 195)+
    # annotate(geom = "text", label = "y = -170 + 0.18x", x = 1990, y = 185)+
    # annotate(geom = "text", label = "y = -140 + 0.15x", x = 1990, y = 173)+
    # annotate(geom = "text", label = "y = 42 + 0.06x", x = 1990, y = 160)+
    # annotate(geom = "text", label = "y = 320 - 0.09x", x = 1990, y = 140)+
    # annotate(geom = "text", label = "y = 850 - 0.37x", x = 1990, y = 115)+
    # annotate(geom = "text", label = "y = 1.4E3 - 0.67x", x = 1990, y = 90)+
    scale_color_viridis_d()+
    scale_fill_viridis_d()+
    labs(title = NULL,
         y = "Mean Length-at-Age (mm)",
         x = "Year")+
    guides(fill = guide_legend(title = "Age", reverse = T), color = guide_legend(title = "Age", reverse = T))+
    theme_bw()
)


ggsave(filename = "~/GitHub/EAGER_growth/Outputs/Figures/Linear Models/lepgib_marginal_effects_plot.tiff", 
       lepgib.marginal.plot, 
       dpi = 300, width = 200, height = 150, units = "mm")
```

## Rainbow Trout

### Model
```{r}
#filter data for only Black Crappie, Pomooxis nigromaculatus 
oncmyk <- all.grow.merge %>% filter(species == "rainbow_trout") %>% 
  filter(age_group %in% c(1, 2, 3, 4), #match age groups in BRTs
         !is.na(age_group), !is.na(begin_date_year), !is.na(length_mean_mm),
         !is.na(log_max_depth), !is.na(logarea))

#linear model
oncmyk.lm <- lm(length_mean_mm ~ begin_date_year*age_group + log_max_depth + logarea + doy, data = oncmyk)
summary(oncmyk.lm)

#calculate and interpret effect sizes
eta_squared(oncmyk.lm)
#interpret(eta_squared(oncmyk.lm), rules = "cohen1992")

#calculate AIC score
AIC(oncmyk.lm)

#examine model fit
testDispersion(oncmyk.lm)

simulation.output <- simulateResiduals(fittedModel = oncmyk.lm)

residuals(oncmyk.lm)
residuals(oncmyk.lm, quantileFunction = qnorm)

plot(oncmyk.lm)
```

### Post-hoc comparisons
```{r}
oncmyk.emm <- emmeans(oncmyk.lm, ~ begin_date_year*age_group)
pairs(oncmyk.emm, simple = "age_group")
test(pairs(oncmyk.emm, by = "begin_date_year"), by = NULL, adjust = "mvt")

#export tables
# #interpret(eta_squared(oncmyk.lm), rules = "cohen1992") %>%
#   write.csv(file = "Outputs/Tables/oncmyk_eta_squared.csv")

#slope contrasts
# Obtain slope estimates
oncmyk.slopes <- emtrends(oncmyk.lm, ~age_group, var = "begin_date_year")
# Compare estimated slopes to 0
oncmyk.slope.contrasts <- test(oncmyk.slopes) %>% 
  mutate(Species = "Rainbow Trout") %>% 
  rename(Age = age_group)

oncmyk.slope.contrasts %>%
  write.csv(file = "Outputs/Tables/oncmyk_emmeans.csv")
```

### Plot raw data
```{r}
(oncmyk.length.year.plot <- ggplot(data = oncmyk %>% 
                              mutate(Age = as.factor(age_group)), 
                            aes(x = begin_date_year, y = length_mean_mm, 
                                color = Age, fill = Age, group = Age))+
    #geom_point()+
    geom_smooth(method = "lm")+
    stat_poly_eq(use_label("eq"))+
    scale_color_viridis_d()+
    scale_fill_viridis_d()+
    labs(y = "Length (mm)", x = "Year")+
    theme_bw()
)

```
### Marginal Effects
```{r}
result <- predict_response(oncmyk.lm, c("begin_date_year", "age_group"))

# plot(result)
# test_predictions(result) %>% 
#   write.csv(file = "Outputs/Tables/oncmyk_pairwise_length_time_slopes.csv", row.names = F)

(oncmyk.marginal.plot <- ggpredict(oncmyk.lm, terms = c("begin_date_year", "age_group")) %>%  
    plot()+
    #stat_poly_eq(use_label("eq"))+
    stat_regline_equation()+
    stat_poly_line()+
    # annotate(geom = "text", label = "y = -5.8E3 + 3.2x", x = 2000, y = 600)+
    # annotate(geom = "text", label = "y = -2.2E3 + 1.3x", x = 2000, y = 475)+
    # annotate(geom = "text", label = "y = -1.3E3 + 0.82x", x = 2000, y = 380)+
    # annotate(geom = "text", label = "y = 920 - 0.33x", x = 2000, y = 285)+
    scale_color_viridis_d()+
    scale_fill_viridis_d()+
    labs(title = NULL,
         y = "Mean Length-at-Age (mm)",
         x = "Year")+
    guides(fill = guide_legend(title = "Age", reverse = T), color = guide_legend(title = "Age", reverse = T))+
    theme_bw()
)


ggsave(filename = "~/GitHub/EAGER_growth/Outputs/Figures/Linear Models/oncmyk_marginal_effects_plot.tiff", 
       oncmyk.marginal.plot, 
       dpi = 300, width = 200, height = 150, units = "mm")
```

## Rock Bass

### Model
```{r}
#filter data for only Black Crappie, Pomooxis nigromaculatus 
ambrup <- all.grow.merge %>% filter(species == "rock_bass") %>% 
  filter(age_group %in% c(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11), #match age groups in BRTs
         !is.na(age_group), !is.na(begin_date_year), !is.na(length_mean_mm),
         !is.na(log_max_depth), !is.na(logarea))

#linear model
ambrup.lm <- lm(length_mean_mm ~ begin_date_year*age_group + log_max_depth + logarea + doy, data = ambrup)
summary(ambrup.lm)

#calculate and interpret effect sizes
eta_squared(ambrup.lm)
#interpret(eta_squared(ambrup.lm), rules = "cohen1992")

#calculate AIC score
AIC(ambrup.lm)

#examine model fit
testDispersion(ambrup.lm)

simulation.output <- simulateResiduals(fittedModel = ambrup.lm)

residuals(ambrup.lm)
residuals(ambrup.lm, quantileFunction = qnorm)

plot(ambrup.lm)
```

### Post-hoc comparisons
```{r}
ambrup.emm <- emmeans(ambrup.lm, ~ begin_date_year*age_group)
pairs(ambrup.emm, simple = "age_group")
test(pairs(ambrup.emm, by = "begin_date_year"), by = NULL, adjust = "mvt")

#export tables
# #interpret(eta_squared(ambrup.lm), rules = "cohen1992") %>%
#   write.csv(file = "Outputs/Tables/ambrup_eta_squared.csv")

#slope contrasts
# Obtain slope estimates
ambrup.slopes <- emtrends(ambrup.lm, ~age_group, var = "begin_date_year")
# Compare estimated slopes to 0
ambrup.slope.contrasts <- test(ambrup.slopes) %>% 
  mutate(Species = "Rock Bass") %>% 
  rename(Age = age_group)

ambrup.slope.contrasts %>%
  write.csv(file = "Outputs/Tables/ambrup_emmeans.csv")
```

### Plot raw data
```{r}
(ambrup.length.year.plot <- ggplot(data = ambrup %>% 
                              mutate(Age = as.factor(age_group)), 
                            aes(x = begin_date_year, y = length_mean_mm, 
                                color = Age, fill = Age, group = Age))+
    #geom_point()+
    geom_smooth(method = "lm")+
    stat_poly_eq(use_label("eq"))+
    scale_color_viridis_d()+
    scale_fill_viridis_d()+
    labs(y = "Length (mm)", x = "Year")+
    theme_bw()
)

```
### Marginal Effects
```{r}
result <- predict_response(ambrup.lm, c("begin_date_year", "age_group"))

# plot(result)
# test_predictions(result) %>% 
#   write.csv(file = "Outputs/Tables/ambrup_pairwise_length_time_slopes.csv", row.names = F)

(ambrup.marginal.plot <- ggpredict(ambrup.lm, terms = c("begin_date_year", "age_group")) %>%  
    plot()+
    #stat_poly_eq(use_label("eq"))+
    stat_regline_equation()+
    stat_poly_line()+
    # annotate(geom = "text", label = "y = 940 - 0.33x", x = 1990, y = 285)+
    # annotate(geom = "text", label = "y = 760 - 0.25x", x = 1990, y = 270)+
    # annotate(geom = "text", label = "y = 430 - 0.09x", x = 1990, y = 258)+
    # annotate(geom = "text", label = "y = 390 - 0.08x", x = 1990, y = 245)+
    # annotate(geom = "text", label = "y = 500 - 0.14x", x = 1990, y = 230)+
    # annotate(geom = "text", label = "y = 210 - 0.004x", x = 1990, y = 215)+
    # annotate(geom = "text", label = "y = 16 + 0.09x", x = 1990, y = 198)+
    # annotate(geom = "text", label = "y = 430 - 0.13x", x = 1990, y = 170)+
    # annotate(geom = "text", label = "y = 580 - 0.22x", x = 1990, y = 145)+
    # annotate(geom = "text", label = "y = 940 - 0.42x", x = 1990, y = 125)+
    # annotate(geom = "text", label = "y = 1.8E3 - 0.85x", x = 1990, y = 90)+
    scale_color_viridis_d()+
    scale_fill_viridis_d()+
    labs(title = NULL,
         y = "Mean Length-at-Age (mm)",
         x = "Year")+
    guides(fill = guide_legend(title = "Age", reverse = T), color = guide_legend(title = "Age", reverse = T))+
    theme_bw()
)


ggsave(filename = "~/GitHub/EAGER_growth/Outputs/Figures/Linear Models/ambrup_marginal_effects_plot.tiff", 
       ambrup.marginal.plot, 
       dpi = 300, width = 200, height = 150, units = "mm")
```




## Smallmouth Bass

### Model
```{r}
#filter data for only Black Crappie, Pomooxis nigromaculatus 
micdol <- all.grow.merge %>% filter(species == "smallmouth_bass") %>% 
  filter(age_group %in% c(0:10), #match age groups in BRTs
         !is.na(age_group), !is.na(begin_date_year), !is.na(length_mean_mm),
         !is.na(log_max_depth), !is.na(logarea))

#linear model
micdol.lm <- lm(length_mean_mm ~ begin_date_year*age_group + log_max_depth + logarea + doy, data = micdol)
summary(micdol.lm)

#calculate and interpret effect sizes
eta_squared(micdol.lm)
#interpret(eta_squared(micdol.lm), rules = "cohen1992")

#calculate AIC score
AIC(micdol.lm)

#examine model fit
testDispersion(micdol.lm)

simulation.output <- simulateResiduals(fittedModel = micdol.lm)

residuals(micdol.lm)
residuals(micdol.lm, quantileFunction = qnorm)

plot(micdol.lm)
```

### Post-hoc comparisons
```{r}
micdol.emm <- emmeans(micdol.lm, ~ begin_date_year*age_group)
pairs(micdol.emm, simple = "age_group")
test(pairs(micdol.emm, by = "begin_date_year"), by = NULL, adjust = "mvt")

#export tables
# #interpret(eta_squared(micdol.lm), rules = "cohen1992") %>%
#   write.csv(file = "Outputs/Tables/micdol_eta_squared.csv")

#slope contrasts
# Obtain slope estimates
micdol.slopes <- emtrends(micdol.lm, ~age_group, var = "begin_date_year")
# Compare estimated slopes to 0
micdol.slope.contrasts <- test(micdol.slopes) %>% 
  mutate(Species = "Smallmouth Bass") %>% 
  rename(Age = age_group)

micdol.slope.contrasts %>%
  write.csv(file = "Outputs/Tables/micdol_emmeans.csv")
```

### Plot raw data
```{r}
(micdol.length.year.plot <- ggplot(data = micdol %>% 
                              mutate(Age = as.factor(age_group)), 
                            aes(x = begin_date_year, y = length_mean_mm, 
                                color = Age, fill = Age, group = Age))+
    #geom_point()+
    geom_smooth(method = "lm")+
    stat_poly_eq(use_label("eq"))+
    scale_color_viridis_d()+
    scale_fill_viridis_d()+
    labs(y = "Length (mm)", x = "Year")+
    theme_bw()
)

```
### Marginal Effects
```{r}
result <- predict_response(micdol.lm, c("begin_date_year", "age_group"))

# plot(result)
# test_predictions(result) %>% 
#   write.csv(file = "Outputs/Tables/micdol_pairwise_length_time_slopes.csv", row.names = F)

(micdol.marginal.plot <- ggpredict(micdol.lm, terms = c("begin_date_year", "age_group")) %>%  
    plot()+
    #stat_poly_eq(use_label("eq"))+
    stat_regline_equation()+
    stat_poly_line()+
    # annotate(geom = "text", label = "y = 77 + 0.07x", x = 2000, y = 224)+
    # annotate(geom = "text", label = "y = 21 + 0.093x", x = 2000, y = 212)+
    # annotate(geom = "text", label = "y = -64 + 0.13x", x = 2000, y = 199)+
    # annotate(geom = "text", label = "y = 230 - 0.028x", x = 2000, y = 182)+
    # annotate(geom = "text", label = "y = 610 - 0.23x", x = 2000, y = 160)+
    # annotate(geom = "text", label = "y = 920 - 0.39x", x = 2000, y = 137)+
    # annotate(geom = "text", label = "y = 1200 - 0.55x", x = 2000, y = 110)+
    # annotate(geom = "text", label = "y = 1700 - 0.83x", x = 2000, y = 80)+
    scale_color_viridis_d()+
    scale_fill_viridis_d()+
    labs(title = NULL,
         y = "Mean Length-at-Age (mm)",
         x = "Year")+
    guides(fill = guide_legend(title = "Age", reverse = T), color = guide_legend(title = "Age", reverse = T))+
    theme_bw()
)


ggsave(filename = "~/GitHub/EAGER_growth/Outputs/Figures/Linear Models/micdol_marginal_effects_plot.tiff", 
       micdol.marginal.plot, 
       dpi = 300, width = 200, height = 150, units = "mm")
```




## Walleye

### Model
```{r}
#filter data for only Black Crappie, Pomooxis nigromaculatus 
sanvit <- all.grow.merge %>% filter(species == "walleye") %>% 
  filter(age_group %in% c(0:12), #match age groups in BRTs
         !is.na(age_group), !is.na(begin_date_year), !is.na(length_mean_mm),
         !is.na(log_max_depth), !is.na(logarea))

#linear model
sanvit.lm <- lm(length_mean_mm ~ begin_date_year*age_group + log_max_depth + logarea + doy, data = sanvit)
summary(sanvit.lm)

#calculate and interpret effect sizes
eta_squared(sanvit.lm)
#interpret(eta_squared(sanvit.lm), rules = "cohen1992")

#calculate AIC score
AIC(sanvit.lm)

#examine model fit
testDispersion(sanvit.lm)

simulation.output <- simulateResiduals(fittedModel = sanvit.lm)

residuals(sanvit.lm)
residuals(sanvit.lm, quantileFunction = qnorm)

plot(sanvit.lm)
```

### Post-hoc comparisons
```{r}
sanvit.emm <- emmeans(sanvit.lm, ~ begin_date_year*age_group)
pairs(sanvit.emm, simple = "age_group")
test(pairs(sanvit.emm, by = "begin_date_year"), by = NULL, adjust = "mvt")

#export tables
# #interpret(eta_squared(sanvit.lm), rules = "cohen1992") %>%
#   write.csv(file = "Outputs/Tables/sanvit_eta_squared.csv")

#slope contrasts
# Obtain slope estimates
sanvit.slopes <- emtrends(sanvit.lm, ~age_group, var = "begin_date_year")
# Compare estimated slopes to 0
sanvit.slope.contrasts <- test(sanvit.slopes) %>% 
  mutate(Species = "Walleye") %>% 
  rename(Age = age_group)

sanvit.slope.contrasts %>%
  write.csv(file = "Outputs/Tables/sanvit_emmeans.csv")
```

### Plot raw data
```{r}
(sanvit.length.year.plot <- ggplot(data = sanvit %>% 
                              mutate(Age = as.factor(age_group)), 
                            aes(x = begin_date_year, y = length_mean_mm, 
                                color = Age, fill = Age, group = Age))+
    #geom_point()+
    geom_smooth(method = "lm")+
    stat_poly_eq(use_label("eq"))+
    scale_color_viridis_d()+
    scale_fill_viridis_d()+
    labs(y = "Length (mm)", x = "Year")+
    theme_bw()
)

```
### Marginal Effects
```{r}
result <- predict_response(sanvit.lm, c("begin_date_year", "age_group"))

# plot(result)
# test_predictions(result) %>% 
#   write.csv(file = "Outputs/Tables/sanvit_pairwise_length_time_slopes.csv", row.names = F)

(sanvit.marginal.plot <- ggpredict(sanvit.lm, terms = c("begin_date_year", "age_group")) %>%  
    plot()+
    #stat_poly_eq(use_label("eq"))+
    stat_regline_equation()+
    stat_poly_line()+
    # annotate(geom = "text", label = "y = 77 + 0.07x", x = 2000, y = 224)+
    # annotate(geom = "text", label = "y = 21 + 0.093x", x = 2000, y = 212)+
    # annotate(geom = "text", label = "y = -64 + 0.13x", x = 2000, y = 199)+
    # annotate(geom = "text", label = "y = 230 - 0.028x", x = 2000, y = 182)+
    # annotate(geom = "text", label = "y = 610 - 0.23x", x = 2000, y = 160)+
    # annotate(geom = "text", label = "y = 920 - 0.39x", x = 2000, y = 137)+
    # annotate(geom = "text", label = "y = 1200 - 0.55x", x = 2000, y = 110)+
    # annotate(geom = "text", label = "y = 1700 - 0.83x", x = 2000, y = 80)+
    scale_color_viridis_d()+
    scale_fill_viridis_d()+
    labs(title = NULL,
         y = "Mean Length-at-Age (mm)",
         x = "Year")+
    guides(fill = guide_legend(title = "Age", reverse = T), color = guide_legend(title = "Age", reverse = T))+
    theme_bw()
)


ggsave(filename = "~/GitHub/EAGER_growth/Outputs/Figures/Linear Models/sanvit_marginal_effects_plot.tiff", 
       sanvit.marginal.plot, 
       dpi = 300, width = 200, height = 150, units = "mm")
```




## Yellow Perch

### Model
```{r}
#filter data for only Black Crappie, Pomooxis nigromaculatus 
perfla <- all.grow.merge %>% filter(species == "yellow_perch") %>% 
  filter(age_group %in% c(0:11), #match age groups in BRTs
         !is.na(age_group), !is.na(begin_date_year), !is.na(length_mean_mm),
         !is.na(log_max_depth), !is.na(logarea))

#linear model
perfla.lm <- lm(length_mean_mm ~ begin_date_year*age_group + log_max_depth + logarea + doy, data = perfla)
summary(perfla.lm)

#calculate and interpret effect sizes
eta_squared(perfla.lm)
#interpret(eta_squared(perfla.lm), rules = "cohen1992")

#calculate AIC score
AIC(perfla.lm)

#examine model fit
testDispersion(perfla.lm)

simulation.output <- simulateResiduals(fittedModel = perfla.lm)

residuals(perfla.lm)
residuals(perfla.lm, quantileFunction = qnorm)

plot(perfla.lm)
```

### Post-hoc comparisons
```{r}
perfla.emm <- emmeans(perfla.lm, ~ begin_date_year*age_group)
pairs(perfla.emm, simple = "age_group")
test(pairs(perfla.emm, by = "begin_date_year"), by = NULL, adjust = "mvt")

#export tables
# #interpret(eta_squared(perfla.lm), rules = "cohen1992") %>%
#   write.csv(file = "Outputs/Tables/perfla_eta_squared.csv")

#slope contrasts
# Obtain slope estimates
perfla.slopes <- emtrends(perfla.lm, ~age_group, var = "begin_date_year")
# Compare estimated slopes to 0
perfla.slope.contrasts <- test(perfla.slopes) %>% 
  mutate(Species = "Yellow Perch") %>% 
  rename(Age = age_group)

perfla.slope.contrasts %>%
  write.csv(file = "Outputs/Tables/perfla_emmeans.csv")
```

### Plot raw data
```{r}
(perfla.length.year.plot <- ggplot(data = perfla %>% 
                              mutate(Age = as.factor(age_group)), 
                            aes(x = begin_date_year, y = length_mean_mm, 
                                color = Age, fill = Age, group = Age))+
    #geom_point()+
    geom_smooth(method = "lm")+
    stat_poly_eq(use_label("eq"))+
    scale_color_viridis_d()+
    scale_fill_viridis_d()+
    labs(y = "Length (mm)", x = "Year")+
    theme_bw()
)

```
### Marginal Effects
```{r}
result <- predict_response(perfla.lm, c("begin_date_year", "age_group"))

# plot(result)
# test_predictions(result) %>% 
#   write.csv(file = "Outputs/Tables/perfla_pairwise_length_time_slopes.csv", row.names = F)

(perfla.marginal.plot <- ggpredict(perfla.lm, terms = c("begin_date_year", "age_group")) %>%  
    plot()+
    #stat_poly_eq(use_label("eq"))+
    stat_regline_equation()+
    stat_poly_line()+
    # annotate(geom = "text", label = "y = 77 + 0.07x", x = 2000, y = 224)+
    # annotate(geom = "text", label = "y = 21 + 0.093x", x = 2000, y = 212)+
    # annotate(geom = "text", label = "y = -64 + 0.13x", x = 2000, y = 199)+
    # annotate(geom = "text", label = "y = 230 - 0.028x", x = 2000, y = 182)+
    # annotate(geom = "text", label = "y = 610 - 0.23x", x = 2000, y = 160)+
    # annotate(geom = "text", label = "y = 920 - 0.39x", x = 2000, y = 137)+
    # annotate(geom = "text", label = "y = 1200 - 0.55x", x = 2000, y = 110)+
    # annotate(geom = "text", label = "y = 1700 - 0.83x", x = 2000, y = 80)+
    scale_color_viridis_d()+
    scale_fill_viridis_d()+
    labs(title = NULL,
         y = "Mean Length-at-Age (mm)",
         x = "Year")+
    guides(fill = guide_legend(title = "Age", reverse = T), color = guide_legend(title = "Age", reverse = T))+
    theme_bw()
)


ggsave(filename = "~/GitHub/EAGER_growth/Outputs/Figures/Linear Models/perfla_marginal_effects_plot.tiff", 
       perfla.marginal.plot, 
       dpi = 300, width = 200, height = 150, units = "mm")
```
# All species marginal effects plot
```{r}
all.spp.marginal.effects <- ggarrange(corart.marginal.plot,
                                      oncmyk.marginal.plot,
                                      saltru.marginal.plot,
                                      perfla.marginal.plot,
                                      esoluc.marginal.plot,
                                      sanvit.marginal.plot,
                                      catcom.marginal.plot,
                                      pomnig.marginal.plot,
                                      ambrup.marginal.plot,
                                      micdol.marginal.plot,
                                      lepgib.marginal.plot,
                                      lepmac.marginal.plot,
                                      common.legend = T,
                                      legend = "right")

all.spp.marginal.effects

salmond.marginal.effects <- ggarrange(corart.marginal.plot,
                                      oncmyk.marginal.plot,
                                      saltru.marginal.plot,
                                      ncol = 1,
                                      common.legend = T,
                                      legend = "right")
```


# Synthesis Plots Across Species

Create tibble of slopes of all species size classes
```{r}
#extracting output from marginal effect plots w/ equations
all.slopes <- tibble(
  Species = c(
    rep("Rock Bass", 11),
    rep("Cisco", 6),
    rep("Northern Pike", 11),
    rep("Pumpkinseed Sunfish", 10),
    rep("Bluegill", 12),
    rep("Smallmouth Bass", 11),
    rep("Largemouth Bass", 13),
    rep("Rainbow Trout", 4),
    rep("Yellow Perch", 12),
    rep("Black Crappie", 12),
    rep("Brown Trout", 5),
    rep("Walleye", 13),
    rep("White Sucker", 5)
  ),
  Age = c(
    c(1:11), #Rock Bass
    c(2:7), #Cisco
    c(0:10), #Northern Pike
    c(1:10), #Pumpkinseed Sunfish
    c(0:11), #Bluegill
    c(0:10), #Smallmouth Bass
    c(0:12), #Largemouth Bass
    c(1:4), #Rainbow Trout
    c(0:11), #Yellow Perch
    c(0:11), #Black Crappie
    c(1:5), #Brown Trout
    c(0:12), #Walleye
    c(1:5) #White Sucker
  ),
  `Thermal Guild` = c(
    rep("Cool", 11), #Rock Bass
    rep("Cold", 6), #Cisco
    rep("Cool", 11), #Northern Pike
    rep("Warm", 10), #Pumpkinseed Sunfish
    rep("Warm", 12), #Bluegill
    rep("Warm", 11), #Smallmouth Bass
    rep("Warm", 13), #Largemouth Bass
    rep("Cold", 4), #Rainbow Trout
    rep("Cool", 12), #Yellow Perch
    rep("Cool", 12), #Black Crappie
    rep("Cold", 5), #Brown Trout
    rep("Cool", 13), #Walleye
    rep("Cool", 5) #White Sucker
    ),
  `Trophic Guild` = c(
    rep("Inv/Carn", 11), #Rock Bass
    rep("Inv/Plank", 6), #Cisco
    rep("Carn", 11), #Northern Pike
    rep("Inv/Carn", 10), #Pumpkinseed Sunfish
    rep("Inv", 12), #Bluegill
    rep("Inv/Carn", 11), #Smallmouth Bass
    rep("Inv/Carn", 13), #Largemouth Bass
    rep("Inv/Carn", 4), #Rainbow Trout
    rep("Inv/Carn", 12), #Yellow Perch
    rep("Inv/Carn", 12), #Black Crappie
    rep("Inv/Carn", 5), #Brown Trout
    rep("Inv/Carn", 13), #Walleye
    rep("Inv/Det", 5) #White Sucker
  )
)  %>% 
  mutate(
         `Life Stage` = case_when(
           Species == "Black Crappie" & Age <= 3 ~ "Juvenile",
           Species == "Black Crappie" & Age > 3 ~ "Adult",
           Species == "White Sucker" & Age <= 4 ~ "Juvenile",
           Species == "White Sucker" & Age > 4 ~ "Adult",
           Species == "Cisco" & Age <= 6 ~ "Juvenile",
           Species == "Cisco" & Age > 6 ~ "Adult",
           Species == "Northern Pike" & Age <= 4 ~ "Juvenile",
           Species == "Northern Pike" & Age > 4 ~ "Adult",
           Species == "Bluegill" & Age <= 4 ~ "Juvenile",
           Species == "Bluegill" & Age > 4 ~ "Adult",
           Species == "Smallmouth Bass" & Age <= 5 ~ "Juvenile",
           Species == "Smallmouth Bass" & Age > 5 ~ "Adult",
           Species == "Largemouth Bass" & Age <= 5 ~ "Juvenile",
           Species == "Largemouth Bass" & Age > 5 ~ "Adult",
           Species == "Rainbow Trout" & Age <= 5 ~ "Juvenile",
           Species == "Rainbow Trout" & Age > 5 ~ "Adult",
           Species == "Black Crappie" & Age <= 3 ~ "Juvenile",
           Species == "Black Crappie" & Age > 3 ~ "Adult",
           Species == "Yellow Perch" & Age <= 4 ~ "Juvenile",
           Species == "Yellow Perch" & Age > 4 ~ "Adult",
           Species == "Pumpkinseed Sunfish" & Age <= 3 ~ "Juvenile",
           Species == "Pumpkinseed Sunfish" & Age > 3 ~ "Adult",
           Species == "Rock Bass" & Age <= 5 ~ "Juvenile",
           Species == "Rock Bass" & Age > 5 ~ "Adult",
           Species == "Brown Trout" & Age <= 5 ~ "Juvenile",
           Species == "Brown Trout" & Age > 5 ~ "Adult",
           Species == "Walleye" & Age <= 5 ~ "Juvenile",
           Species == "Walleye" & Age > 5 ~ "Adult"
         ), Age = as.factor(Age)) %>% 
  left_join(mean.length.age.class %>% 
              rename(mean_length = mean), 
            by = c("Species", "Age")) %>% 
  left_join(
    bind_rows(
      pomnig.slope.contrasts,
      lepmac.slope.contrasts,
      saltru.slope.contrasts,
      corart.slope.contrasts,
      catcom.slope.contrasts,
      micsal.slope.contrasts,
      esoluc.slope.contrasts,
      lepgib.slope.contrasts,
      oncmyk.slope.contrasts,
      ambrup.slope.contrasts,
      micdol.slope.contrasts,
      sanvit.slope.contrasts,
      perfla.slope.contrasts
    ) %>% 
      rename(Slope = begin_date_year.trend),
    by = c("Species", "Age")
  ) %>% 
  mutate(`Percent Change` = (Slope/mean_length)*100,
         `p-value` = if_else(p.value <= 0.05, "\u2264 0.05", "> 0.05"),
         spp_code = case_when(
           Species == "Black Crappie" ~ "BCR",
           Species == "Bluegill" ~ "BLG", 
           Species == "Brown Trout" ~ "BNT",
           Species == "Cisco" ~ "CIS",
           Species == "Largemouth Bass" ~ "LMB",
           Species == "Northern Pike" ~ "NOP",
           Species == "Pumpkinseed Sunfish" ~ "PSF",
           Species == "Rainbow Trout" ~ "RBT",
           Species == "Rock Bass" ~ "RKB",
           Species == "Smallmouth Bass" ~ "SMB",
           Species == "Walleye" ~ "WAE",
           Species == "White Sucker" ~ "CWS",
           Species == "Yellow Perch" ~ "YEP"
         ),
         FTP = case_when(
           Species == "Black Crappie" ~ 23.4,
           Species == "Bluegill" ~ 30.2, 
           Species == "Brown Trout" ~ 15.7,
           Species == "Cisco" ~ 12.4,
           Species == "Largemouth Bass" ~ 28.6,
           Species == "Northern Pike" ~ 20.7,
           Species == "Pumpkinseed Sunfish" ~ 27.7,
           Species == "Rainbow Trout" ~ 15.5,
           Species == "Rock Bass" ~ 24.9,
           Species == "Smallmouth Bass" ~ 25.0,
           Species == "Walleye" ~ 22.5,
           Species == "White Sucker" ~ 23.4,
           Species == "Yellow Perch" ~ 17.6
         ),
         CTmax = case_when(
           Species == "Black Crappie" ~ 34.9,
           Species == "Bluegill" ~ 40.2, 
           Species == "Brown Trout" ~ 28.3,
           Species == "Cisco" ~ 26.9,
           Species == "Largemouth Bass" ~ 38.4,
           Species == "Northern Pike" ~ 32.6,
           Species == "Pumpkinseed Sunfish" ~ 37.6,
           Species == "Rainbow Trout" ~ 22.1,
           Species == "Rock Bass" ~ 36.0,
           Species == "Smallmouth Bass" ~ 36.3,
           Species == "Walleye" ~ 23.4,
           Species == "White Sucker" ~ 31.6,
           Species == "Yellow Perch" ~ 35.0
         ),
         Species = fct_reorder(Species, FTP),
         scaled_slope = scale(Slope),
         log_slope = log(Slope+abs(min(Slope))+1)
         )

#write out reduced version of the table
write.csv(all.slopes %>% 
            select(-c(`Trophic Guild`, species, `p-value`, spp_code, scaled_slope, log_slope)),
            file = "Outputs/Tables/all_slopes.csv",
          row.names = F)
```

Some summary statistics
```{r}
#mean slope per thermal guild
mean.mm.yr.thermal.guild <- all.slopes %>% 
  filter(`p-value` == "≤ 0.05") %>% 
  group_by(`Thermal Guild`) %>% 
  summarise(mean_Slope = mean(Slope), mean_Percent = mean(`Percent Change`))

#per species
mean.mm.yr.species <- all.slopes %>% 
  filter(`p-value` == "≤ 0.05") %>% 
  group_by(Species) %>% 
  summarise(mean_Slope = mean(Slope), mean_Percent = mean(`Percent Change`))

mean.mm.yr.species.lifestage <- all.slopes %>% 
  filter(`p-value` == "≤ 0.05") %>% 
  group_by(Species, `Life Stage`) %>% 
  summarise(mean_Slope = mean(Slope), mean_Percent = mean(`Percent Change`))

mean.mm.year.life.stage <- all.slopes %>% 
  filter(`p-value` == "≤ 0.05") %>% 
  group_by(`Thermal Guild`, `Life Stage`) %>% 
  summarise(mean_Slope = mean(Slope), mean_Percent = mean(`Percent Change`)) %>% 
  arrange(mean_Slope)

mean.mm.age <- all.slopes %>% 
  filter(`p-value` == "≤ 0.05") %>% 
  group_by(Age) %>% 
  summarise(mean_Slope = mean(Slope), mean_Percent = mean(`Percent Change`)) %>% 
  arrange(mean_Slope)  
  
#number of species age classes statistically increasing
all.slopes %>% 
  filter(`p-value` == "≤ 0.05") %>% 
  filter(Slope > 0) %>% 
  nrow()
#15

all.slopes %>% 
  #filter(`p-value` == "≤ 0.05") %>% 
  filter(Slope < 0, 
         `Thermal Guild` == "Warm") %>% 
  nrow()
#28

#number of species age classes statistically decreasing
all.slopes %>% 
  filter(`p-value` == "≤ 0.05") %>% 
  filter(Slope < 0) %>% 
  nrow()
#52
```

##Plot
```{r}
all.slopes.CI <- all.slopes %>%
  group_by(Species) %>%
  summarize(
    mean = mean(Slope),
    sd = sd(Slope),
    n = n(),
    se = sd / sqrt(n),
    ci_lower = mean - qt(0.975, n - 1) * se,
    ci_upper = mean + qt(0.975, n - 1) * se
  )

(
  slopes.all.plot <- ggplot() +
    geom_hline(
      yintercept = 0,
      color = "red",
      linetype = "dashed"
    ) +
    
    #geom_boxplot(fill = "white", color = "black")+
    geom_errorbar(
      data = all.slopes.CI,
      aes(x = Species, ymin = ci_lower, ymax = ci_upper),
      width = 0.2
    ) +
    geom_point(data = all.slopes.CI, aes(x = Species, y = mean), size = 3) +
    geom_jitter(
      data = all.slopes,
      aes(
        x = Species,
        y = Slope,
        color = `Thermal Guild`,
        fill = `Thermal Guild`,
        shape = `p-value`,
        group = Species
      ),
      size = 2,
      width = 0.2
    ) +
    # scale_x_discrete(limits = c("Cisco", "Rainbow Trout", "Brown Trout", "Black Crappie",
    #                             "Rock Bass", "Walleye", "Yellow Perch", "Northern Pike", "White Sucker",
    #                             "Largemouth Bass", "Smallmouth Bass", "Pumpkinseed Sunfish",
    #                             "Bluegill"))+
    scale_color_viridis_d() +
    scale_fill_viridis_d() +
    scale_shape_manual(values = c(2, 21)) +
    labs(y = "Change in Length-at-Age (mm/yr)", x = NULL) +
    # annotate(
    #   geom = "text",
    #   x = 1,
    #   y = 2.75,
    #   label = 0.62
    # ) +
    # annotate(
    #   geom = "text",
    #   x = 2,
    #   y = 2.75,
    #   label = 2.19
    # ) +
    # annotate(
    #   geom = "text",
    #   x = 4,
    #   y = 2.75,
    #   label = -0.31
    # ) +
    # annotate(
    #   geom = "text",
    #   x = 5,
    #   y = 2.75,
    #   label = -1.84
    # ) +
    # annotate(
    #   geom = "text",
    #   x = 6,
    #   y = 2.75,
    #   label = -0.66
    # ) +
    # annotate(
    #   geom = "text",
    #   x = 7,
    #   y = 2.75,
    #   label = -0.31
    # ) +
    # annotate(
    #   geom = "text",
    #   x = 8,
    #   y = 2.75,
    #   label = -0.93
    # ) +
    # annotate(
    #   geom = "text",
    #   x = 9,
    #   y = 2.75,
    #   label = -0.40
    # ) +
    # annotate(
    #   geom = "text",
    #   x = 10,
    #   y = 2.75,
    #   label = -0.50
    # ) +
    # annotate(
    #   geom = "text",
    #   x = 11,
    #   y = 2.75,
    #   label = 0.02
    # ) +
    # annotate(
    #   geom = "text",
    #   x = 12,
    #   y = 2.75,
    #   label = -0.59
    # ) +
    # annotate(
    #   geom = "text",
    #   x = 13,
    #   y = 2.75,
    #   label = -0.12
    # ) +
    theme_bw()+
    theme(
      #panel.background = element_rect(fill = "gray"),
      axis.text.x = element_text(
        angle = 45,
        hjust = 1,
        size = 12
      ),
      axis.text.y = element_text(size = 10),
      axis.title.x = element_text(size = 12)
    )
)

ggsave(filename = "~/GitHub/EAGER_growth/Outputs/Figures/Linear Models/all_slopes_thermal.tiff", 
       slopes.all.plot, 
       dpi = 300, width = 200, height = 150, units = "mm")
```


```{r}

all.slopes.pc.CI <- all.slopes %>%
  group_by(Species) %>%
  summarize(
    mean = mean(`Percent Change`),
    sd = sd(`Percent Change`),
    n = n(),
    se = sd / sqrt(n),
    ci_lower = mean - qt(0.975, n - 1) * se,
    ci_upper = mean + qt(0.975, n - 1) * se
  )

(
  slopes.all.plot.percent <- ggplot() +
    geom_hline(
      yintercept = 0,
      color = "red",
      linetype = "dashed"
    ) +
    #geom_boxplot(fill = "white", color = "black") +
    geom_errorbar(data = all.slopes.pc.CI,
                  aes(x = Species, ymin = ci_lower, ymax = ci_upper),
                  width = 0.2) +
    geom_point(data = all.slopes.pc.CI, aes(x = Species, y = mean), size = 3) +
    geom_point(
      data = all.slopes ,
      aes(
        x = Species,
        y = `Percent Change`,
        color = `Thermal Guild`,
        fill = `Thermal Guild`,
        shape = `p-value`,
        group = Species
      ),
      size = 2
    ) +
    # scale_x_discrete(limits = c("Cisco", "Rainbow Trout", "Brown Trout", "Black Crappie",
    #                             "Rock Bass", "Walleye", "Yellow Perch", "Northern Pike", "White Sucker",
    #                             "Largemouth Bass", "Smallmouth Bass", "Pumpkinseed Sunfish", "Bluegill"))+
    scale_color_viridis_d() +
    scale_fill_viridis_d() +
    scale_shape_manual(values = c(2, 16)) +
    geom_text_repel(data = all.slopes,
      aes(x = Species, y = `Percent Change`, label = Age),
      show.legend = F,
      max.overlaps = 100,
      max.iter = 100000
    ) +
    # annotate(
    #   geom = "text",
    #   x = 1,
    #   y = 0.9,
    #   label = 0.18,
    #   size = 3
    # ) +
    # annotate(
    #   geom = "text",
    #   x = 2,
    #   y = 0.9,
    #   label = 0.51,
    #   size = 3
    # ) +
    # annotate(
    #   geom = "text",
    #   x = 4,
    #   y = 0.9,
    #   label = -0.23,
    #   size = 3
    # ) +
    # annotate(
    #   geom = "text",
    #   x = 5,
    #   y = 0.9,
    #   label = -0.30,
    #   size = 3
    # ) +
    # annotate(
    #   geom = "text",
    #   x = 6,
    #   y = 0.9,
    #   label = -0.15,
    #   size = 3
    # ) +
    # annotate(
    #   geom = "text",
    #   x = 7,
    #   y = 0.9,
    #   label = -0.16,
    #   size = 3
    # ) +
    # annotate(
    #   geom = "text",
    #   x = 8,
    #   y = 0.9,
    #   label = -0.24,
    #   size = 3
    # ) +
    # annotate(
    #   geom = "text",
    #   x = 9,
    #   y = 0.9,
    #   label = -0.45,
    #   size = 3
    # ) +
    # annotate(
    #   geom = "text",
    #   x = 10,
    #   y = 0.9,
    #   label = -0.32,
    #   size = 3
    # ) +
    # annotate(
    #   geom = "text",
    #   x = 11,
    #   y = 0.9,
    #   label = -0.07,
    #   size = 3
    # ) +
    # annotate(
    #   geom = "text",
    #   x = 12,
    #   y = 0.9,
    #   label = -0.24,
    #   size = 3
    # ) +
    # annotate(
    #   geom = "text",
    #   x = 13,
    #   y = 0.9,
    #   label = -0.21,
    #   size = 3
    # ) +
    labs(y = "Annual Percent Change in Length-at-Age", x = NULL) +
    theme_bw()+
    theme(
      #panel.background = element_rect(fill = "gray"),
      ,
      axis.text.x = element_text(
        angle = 45,
        hjust = 1,
        size = 12
      ),
      axis.text.y = element_text(size = 10),
      axis.title.x = element_text(size = 12)
    )
)

ggsave(filename = "~/GitHub/EAGER_growth/Outputs/Figures/Linear Models/all_slopes_percent_thermal.tiff", 
       slopes.all.plot.percent, 
       dpi = 300, width = 200, height = 150, units = "mm")
```

Shape by life stage
```{r}

all.slopes.life.stage.CI <- all.slopes %>%
  group_by(Species, `Life Stage`) %>%
  summarize(
    mean = mean(`Percent Change`),
    sd = sd(`Percent Change`),
    n = n(),
    se = sd / sqrt(n),
    ci_lower = mean - qt(0.975, n - 1) * se,
    ci_upper = mean + qt(0.975, n - 1) * se
  ) %>% 
  mutate(`Life Stage` = fct_rev(`Life Stage`))

(
  slopes.all.plot.percent.life.stage <- ggplot() +
    geom_hline(
      yintercept = 0,
      color = "red",
      linetype = "dashed"
    ) +
    # geom_boxplot(
    #   fill = "white",
    #   color = "black",
    #   outliers = F
    # ) +
    geom_errorbar(
      data = all.slopes.life.stage.CI,
      aes(
        x = Species,
        ymin = ci_lower,
        ymax = ci_upper,
        group = interaction(`Life Stage`, Species)
      ),
      position = position_dodge(width = 0.4),
      width = 0.2
    ) +
    geom_point(data = all.slopes.life.stage.CI,
               aes(
                 x = Species,
                 y = mean,
                 group = interaction(`Life Stage`, Species)
               ),
               position = position_dodge(width = 0.4),
               size = 3) +
    geom_jitter(
      data = all.slopes %>%
        mutate(`Life Stage` = fct_rev(`Life Stage`)),
      aes(
        x = Species,
        y = `Percent Change`,
        color = `Thermal Guild`,
        fill = `Thermal Guild`,
        shape = `p-value`,
        group = interaction(`Life Stage`, Species)
      ),
      position = position_dodge(width = 0.4),
      size = 2
    ) +
    # scale_x_discrete(limits = c("Cisco", "Rainbow Trout", "Brown Trout", "Black Crappie",
    #                             "Rock Bass", "Walleye", "Yellow Perch", "Northern Pike", "White Sucker",
    #                             "Largemouth Bass", "Smallmouth Bass", "Pumpkinseed Sunfish", "Bluegill"))+
    scale_color_viridis_d() +
    scale_fill_viridis_d() +
    scale_shape_manual(values = c(2, 16)) +
    labs(y = "Annual Percent Change in Length-at-Age", x = NULL) +
    # annotate(
    #   geom = "text",
    #   x = 0.75,
    #   y = 0.3,
    #   label = 0.18,
    #   size = 2
    # ) +
    # annotate(
    #   geom = "text",
    #   x = 2,
    #   y = 0.6,
    #   label = 0.51,
    #   size = 2
    # ) +
    # annotate(
    #   geom = "text",
    #   x = 3.75,
    #   y = 0.1,
    #   label = -0.31,
    #   size = 2
    # ) +
    # annotate(
    #   geom = "text",
    #   x = 4.25,
    #   y = 0.2,
    #   label = -0.11,
    #   size = 2
    # ) +
    # annotate(
    #   geom = "text",
    #   x = 4.75,
    #   y = 0.1,
    #   label = -0.26,
    #   size = 2
    # ) +
    # annotate(
    #   geom = "text",
    #   x = 5.25,
    #   y = 0.01,
    #   label = -0.33,
    #   size = 2
    # ) +
    # annotate(
    #   geom = "text",
    #   x = 5.75,
    #   y = 0.15,
    #   label = -0.36,
    #   size = 2
    # ) +
    # annotate(
    #   geom = "text",
    #   x = 6.25,
    #   y = 0.25,
    #   label = -0.10,
    #   size = 2
    # ) +
    # annotate(
    #   geom = "text",
    #   x = 6.75,
    #   y = 0.1,
    #   label = -0.21,
    #   size = 2
    # ) +
    # annotate(
    #   geom = "text",
    #   x = 7.25,
    #   y = 0.2,
    #   label = -0.08,
    #   size = 2
    # ) +
    # annotate(
    #   geom = "text",
    #   x = 7.75,
    #   y = -0.03,
    #   label = -0.24,
    #   size = 2
    # ) +
    # annotate(
    #   geom = "text",
    #   x = 8.75,
    #   y = 0.1,
    #   label = -0.45,
    #   size = 2
    # ) +
    # annotate(
    #   geom = "text",
    #   x = 9.75,
    #   y = 0.15,
    #   label = -0.32,
    #   size = 2
    # ) +
    # annotate(
    #   geom = "text",
    #   x = 10.75,
    #   y = 0.01,
    #   label = -0.54,
    #   size = 2
    # ) +
    # annotate(
    #   geom = "text",
    #   x = 11.25,
    #   y = 0.25,
    #   label = 0.12,
    #   size = 2
    # ) +
    # annotate(
    #   geom = "text",
    #   x = 11.75,
    #   y = -0.03,
    #   label = -0.30,
    #   size = 2
    # ) +
    # annotate(
    #   geom = "text",
    #   x = 12.25,
    #   y = 0.05,
    #   label = -0.17,
    #   size = 2
    # ) +
    # annotate(
    #   geom = "text",
    #   x = 12.75,
    #   y = 0.15,
    #   label = -0.45,
    #   size = 2
    # ) +
    # annotate(
    #   geom = "text",
    #   x = 13.25,
    #   y = 0.25,
    #   label = 0.09,
    #   size = 2
    # ) +
    theme_bw()+
    theme(
      #panel.background = element_rect(fill = "gray"),
      ,
      axis.text.x = element_text(
        angle = 45,
        hjust = 1,
        size = 12
      ),
      axis.text.y = element_text(size = 10),
      axis.title.x = element_text(size = 12)
    )
)

ggsave(filename = "~/GitHub/EAGER_growth/Outputs/Figures/Linear Models/all_slopes_percent_thermal_life_stage.tiff", 
       slopes.all.plot.percent.life.stage, 
       dpi = 300, width = 200, height = 150, units = "mm")
```

Break adults and juveniles into their own panels
Juveniles
```{r}
(
  slopes.all.plot.percent.juvenile <- ggplot(data = all.slopes.life.stage.CI %>% 
                                                 filter(`Life Stage` == "Juvenile")) +
    geom_hline(
      yintercept = 0,
      color = "red",
      linetype = "dashed"
    ) +
    geom_errorbar(
      aes(
        x = Species,
        ymin = ci_lower,
        ymax = ci_upper,
        group = interaction(`Life Stage`, Species)
      ),
      position = position_dodge(width = 0.4),
      width = 0.2
    ) +
    geom_point(
               aes(
                 x = Species,
                 y = mean,
                 group = interaction(`Life Stage`, Species)
               ),
               position = position_dodge(width = 0.4),
               size = 3) +
    geom_jitter(
      data = all.slopes %>%
        filter(`Life Stage` == "Juvenile"),
      aes(
        x = Species,
        y = `Percent Change`,
        color = `Thermal Guild`,
        fill = `Thermal Guild`,
        shape = `p-value`,
        #group = interaction(`Life Stage`, Species)
      ),
      position = position_dodge(width = 0.4),
      size = 2
    ) +
    scale_color_viridis_d() +
    scale_fill_viridis_d() +
    scale_shape_manual(values = c(2, 16)) +
    labs(y = "Annual Percent Change in Length-at-Age", 
         x = NULL,
         title = "Juveniles") +
    theme_bw()+
    theme(
      #panel.background = element_rect(fill = "gray"),
      ,
      axis.text.x = element_text(
        angle = 45,
        hjust = 1,
        size = 12
      ),
      axis.text.y = element_text(size = 10),
      axis.title.x = element_text(size = 12)
    )
)
```

Juveniles
```{r}
(
  slopes.all.plot.percent.adult <- ggplot(data = all.slopes.life.stage.CI %>% 
                                                 filter(`Life Stage` == "Adult")) +
    geom_hline(
      yintercept = 0,
      color = "red",
      linetype = "dashed"
    ) +
    geom_errorbar(
      aes(
        x = Species,
        ymin = ci_lower,
        ymax = ci_upper,
        group = interaction(`Life Stage`, Species)
      ),
      position = position_dodge(width = 0.4),
      width = 0.2
    ) +
    geom_point(
               aes(
                 x = Species,
                 y = mean,
                 group = interaction(`Life Stage`, Species)
               ),
               position = position_dodge(width = 0.4),
               size = 3) +
    geom_jitter(
      data = all.slopes %>%
        filter(`Life Stage` == "Adult"),
      aes(
        x = Species,
        y = `Percent Change`,
        color = `Thermal Guild`,
        fill = `Thermal Guild`,
        shape = `p-value`,
        #group = interaction(`Life Stage`, Species)
      ),
      position = position_dodge(width = 0.4),
      size = 2
    ) +
    scale_color_viridis_d() +
    scale_fill_viridis_d() +
    scale_shape_manual(values = c(2, 16)) +
    labs(y = "Annual Percent Change in Length-at-Age", 
         x = NULL,
         title = "Adults") +
    theme_bw()+
    theme(
      #panel.background = element_rect(fill = "gray"),
      ,
      axis.text.x = element_text(
        angle = 45,
        hjust = 1,
        size = 12
      ),
      axis.text.y = element_text(size = 10),
      axis.title.x = element_text(size = 12)
    )
)
```

Plot by age class instead of species
```{r}
all.slopes.age.CI <- all.slopes %>%
  group_by(Age) %>%
  summarize(
    mean = mean(`Percent Change`),
    sd = sd(`Percent Change`),
    n = n(),
    se = sd / sqrt(n),
    ci_lower = mean - qt(0.975, n - 1) * se,
    ci_upper = mean + qt(0.975, n - 1) * se
  ) 

(
  slopes.all.plot.percent.age.class <- ggplot() +
    geom_hline(
      yintercept = 0,
      color = "red",
      linetype = "dashed"
    ) +
    # geom_boxplot(
    #   fill = "white",
    #   color = "black",
    #   outliers = F
    # ) +
    geom_errorbar(
      data = all.slopes.age.CI,
      aes(
        x = Age,
        ymin = ci_lower,
        ymax = ci_upper
      ),
      position = position_dodge(width = 0.4),
      width = 0.2
    ) +
    geom_point(data = all.slopes.age.CI,
               aes(
                 x = Age,
                 y = mean,
               ),
               position = position_dodge(width = 0.4),
               size = 3) +
    geom_jitter(data = all.slopes ,
    aes(
      x = Age,
      y = `Percent Change`,
      color = `Thermal Guild`,
      fill = `Thermal Guild`,
      shape = `p-value`
    ), 
    width = 0.2, 
    size = 2) +
    #geom_text(aes(label = spp_code))+
    # scale_x_discrete(limits = c("Cisco", "Rainbow Trout", "Brown Trout", "Black Crappie",
    #                             "Rock Bass", "Walleye", "Yellow Perch", "Northern Pike", "White Sucker",
    #                             "Largemouth Bass", "Smallmouth Bass", "Pumpkinseed Sunfish", "Bluegill"))+
    scale_color_viridis_d() +
    scale_fill_viridis_d() +
    scale_shape_manual(values = c(2, 16)) +
    labs(y = "Annual Percent Change in Length-at-Age") +
    # annotate(
    #   geom = "text",
    #   x = 1,
    #   y = 0.05,
    #   label = -0.66,
    #   size = 3
    # ) +
    # annotate(
    #   geom = "text",
    #   x = 2,
    #   y = 0.25,
    #   label = -0.57,
    #   size = 3
    # ) +
    # annotate(
    #   geom = "text",
    #   x = 3,
    #   y = 0.5,
    #   label = -0.18,
    #   size = 3
    # ) +
    # annotate(
    #   geom = "text",
    #   x = 4,
    #   y = 0.5,
    #   label = -0.08,
    #   size = 3
    # ) +
    # annotate(
    #   geom = "text",
    #   x = 5,
    #   y = 0.5,
    #   label = 0.04,
    #   size = 3
    # ) +
    # annotate(
    #   geom = "text",
    #   x = 6,
    #   y = 0.5,
    #   label = 0.01,
    #   size = 3
    # ) +
    # annotate(
    #   geom = "text",
    #   x = 7,
    #   y = 0.5,
    #   label = -0.03,
    #   size = 3
    # ) +
    # annotate(
    #   geom = "text",
    #   x = 8,
    #   y = 0.5,
    #   label = -0.09,
    #   size = 3
    # ) +
    # annotate(
    #   geom = "text",
    #   x = 9,
    #   y = 0.5,
    #   label = -0.12,
    #   size = 3
    # ) +
    # annotate(
    #   geom = "text",
    #   x = 10,
    #   y = 0.5,
    #   label = -0.23,
    #   size = 3
    # ) +
    # annotate(
    #   geom = "text",
    #   x = 11,
    #   y = 0.5,
    #   label = -0.21,
    #   size = 3
    # ) +
    # annotate(
    #   geom = "text",
    #   x = 13,
    #   y = 0.5,
    #   label = -0.18,
    #   size = 3
    # ) +
    theme_bw()+
    theme(
      #panel.background = element_rect(fill = "gray"),
      #axis.text.x = element_text(angle = 45, hjust = 1, size = 12),
      axis.text.y = element_text(size = 10),
      axis.title.x = element_text(size = 12)
    )
)

ggsave(filename = "~/GitHub/EAGER_growth/Outputs/Figures/Linear Models/all_slopes_percent_age_class.tiff", 
       slopes.all.plot.percent.age.class, 
       dpi = 300, width = 200, height = 150, units = "mm")
```

```{r}
(slopes.all.plot.percent.age.class.spp.facet <- ggplot(data = all.slopes , 
                           aes(x = Age, y = `Percent Change`, 
                               color = `Thermal Guild`, 
                               fill = `Thermal Guild`,
                               shape = `p-value`,
                               group = Age
                               ))+
  geom_hline(yintercept = 0, color = "red", linetype = "dashed")+
  geom_point(size = 2)+
  facet_wrap(~Species)+
  scale_color_viridis_d()+
  scale_fill_viridis_d()+
  scale_shape_manual(values = c(2, 16))+ 
  labs(y = "Annual Percent Change in Length-at-Age")+
  theme_bw()+
  theme(#panel.background = element_rect(fill = "gray"),
        #axis.text.x = element_text(angle = 45, hjust = 1, size = 12),
        axis.text.y = element_text(size = 8), axis.title.x = element_text(size = 10),
        axis.text.x = element_text(size = 8),
        strip.text = element_text(size = 8))
)

ggsave(filename = "~/GitHub/EAGER_growth/Outputs/Figures/Linear Models/all_slopes_percent_age_class_spp_facet.tiff", 
       slopes.all.plot.percent.age.class.spp.facet, 
       dpi = 300, width = 200, height = 150, units = "mm")
```

Multi-panel figure to include in text that shows trends by species and by age class
```{r}
age.class.species.multi.panel.plot <- ggarrange(slopes.all.plot.percent.age.class, 
                                                slopes.all.plot.percent.age.class.spp.facet,
                                                slopes.all.plot.percent,
                                                slopes.all.plot.percent.life.stage,
                                                ncol = 2, nrow = 2, 
                                                common.legend = T, legend = "right",
                                                labels = "AUTO", label.x = 0.04)

age.class.species.multi.panel.plot

ggsave(filename = "~/GitHub/EAGER_growth/Outputs/Figures/Linear Models/age_class_species_multi-panel_plot.tiff", 
       age.class.species.multi.panel.plot, 
       dpi = 300, width = 267, height = 200, units = "mm")

```

Two-panel version
```{r}
age.class.species.two.panel.plot <- ggarrange(
  slopes.all.plot.percent.age.class,
  slopes.all.plot.percent.life.stage,
  ncol = 1,
  nrow = 2,
  common.legend = T,
  legend = "right",
  labels = "AUTO",
  label.x = 0.03
)

age.class.species.two.panel.plot

ggsave(filename = "~/GitHub/EAGER_growth/Outputs/Figures/Linear Models/age_class_species_two-panel_plot.tiff", 
       age.class.species.two.panel.plot, 
       dpi = 300, width = 200, height = 250, units = "mm")
```

# Model of slopes with FTP
## all slopes

Visualize the relationships
```{r}
#Check assumptions
#linear relationships
(slopes.linear.plot <- ggscatter(data = all.slopes, x = "FTP", y = "Percent Change", color = "Life Stage", add = "reg.line")+
  stat_regline_equation(aes(label =  paste(..eq.label.., ..rr.label.., sep = "~~~~"), color = `Life Stage`))+
  scale_color_viridis_d(end = 0.8)
)

# ggsave(filename = "~/GitHub/EAGER_growth/Outputs/Figures/Linear Models/all_slopes_linear_relationships.tiff", 
#        slopes.linear.plot, 
#        dpi = 300, width = 200, height = 150, units = "mm")
```

Check assumptions
```{r}
#homogeneity of regression slopes
anova_test(`Percent Change` ~ FTP*`Life Stage`, data = all.slopes) # not ideal

slopes.model <- lm(`Percent Change` ~ FTP*`Life Stage`, data = all.slopes)
summary(slopes.model)

slopes.model.metric <- augment(slopes.model) 

#normality of residuals
slopes.shapiro <- shapiro_test(slopes.model.metric$.resid)
slopes.shapiro # p < 0.05 
hist(scale(all.slopes$`Percent Change`))

#homogeneity of variances
slopes.levene <- slopes.model.metric %>% levene_test(.resid ~ `Life Stage`)
slopes.levene # p = 0.43 => meets assumptions

#outliers
slopes.model.metric %>% 
  filter(abs(.std.resid) > 3) %>%
  as.data.frame() #no outliers

res.aov <- all.slopes %>% anova_test(`Percent Change` ~ FTP + `Life Stage`)
get_anova_table(res.aov)

pwc <- all.slopes %>% 
  rename(life_stage  = `Life Stage`,
         percent_change = `Percent Change`) %>% 
  emmeans_test(
    percent_change ~ life_stage, covariate = FTP,
    p.adjust.method = "bonferonni"
    )
pwc

get_emmeans(pwc)
```

Visualize pairwise comparisons 
```{r}
# Visualization: line plots with p-values
pwc <- pwc %>% add_xy_position(x = "life_stage", fun = "mean_se")

(slopes.ancova.emmeans.plot <- ggline(get_emmeans(pwc), x = "life_stage", y = "emmean") +
  geom_errorbar(aes(ymin = conf.low, ymax = conf.high), width = 0.2) + 
  stat_pvalue_manual(pwc, hide.ns = TRUE, tip.length = FALSE) +
  labs(
    subtitle = get_test_label(res.aov, detailed = TRUE),
    caption = get_pwc_label(pwc),
    x = "Life Stage", y = "Estimated Marginal Means")
)

# ggsave(filename = "~/GitHub/EAGER_growth/Outputs/Figures/Linear Models/all_slopes_percent_age_class_spp_facet.tiff", 
#        slopes.ancova.emmeans.plot, 
#        dpi = 300, width = 200, height = 150, units = "mm")
```

Stack plots
```{r}
all.slopes.ancova.stack <- ggarrange(slopes.linear.plot, slopes.ancova.emmeans.plot,
                                     ncol = 1, nrow  = 2,
                                     labels = "AUTO")

all.slopes.ancova.stack

ggsave(filename = "~/GitHub/EAGER_growth/Outputs/Figures/Linear Models/all_slopes_ancova_stack.tiff", 
       all.slopes.ancova.stack, 
       dpi = 300, width = 200, height = 150, units = "mm")
```

## sig diff from 0 slopes
Visualize the relationship
```{r}
all.slopes.sig.dif <- all.slopes %>% filter(p.value < 0.05)

#calculate quantiles
all.slopes.sig.dif.quants <- quantile(all.slopes.sig.dif$`Percent Change`, c(0.025, 0.975))

#identify points outside 2.5% and 97.5% quantiles
all.slopes.sig.dif <- all.slopes.sig.dif %>% 
  mutate(Outlier = case_when(
    Slope < -0.8107416 ~ "Below 2.5%",
    Slope > -0.8107416 & Slope < 0.3922495  ~ "Not outliers",
    Slope > 0.3922495  ~ "Above 97.5%"
  ))
#linear relationships
(slopes.sig.dif.linear.plot <- ggscatter(data = all.slopes.sig.dif, 
                                        x = "FTP", y = "Percent Change", 
                                        color = "Life Stage", add = "reg.line"
                                        #, shape = "Outlier"
                                        )+
  stat_regline_equation(aes(label =  paste(..eq.label.., ..rr.label.., sep = "~~~~"), color = `Life Stage`))+
  scale_color_viridis_d(end = 0.8)+
  scale_shape_manual(values = c(1, 2, 19))
)
```

Check assumptions
```{r}
#homogeneity of regression slopes
anova_test(`Percent Change` ~ FTP*`Life Stage`, data = all.slopes.sig.dif) # not ideal

slopes.model.sig.dif <- lm(`Percent Change` ~ FTP*`Life Stage`, data = all.slopes.sig.dif)
summary(slopes.model.sig.dif)

slopes.model.metric.sig.dif <- augment(slopes.model.sig.dif) 

#normality of residuals
slopes.shapiro.sig.dif <- shapiro_test(slopes.model.metric.sig.dif$.resid)
slopes.shapiro.sig.dif # p = 0.001 => violates assumptions

#homogeneity of variances
slopes.levene.sig.dif <- slopes.model.metric.sig.dif %>% levene_test(.resid ~ `Life Stage`)
slopes.levene.sig.dif # p = 0.17 => meets assumptions

#outliers
slopes.model.metric.sig.dif %>% 
  filter(abs(.std.resid) > 3) %>%
  as.data.frame() #two outliers

res.aov.sig.dif <- all.slopes.sig.dif %>% anova_test(`Percent Change` ~ FTP + `Life Stage`)
get_anova_table(res.aov.sig.dif)

pwc.sig.dif <- all.slopes.sig.dif %>% 
  rename(life_stage  = `Life Stage`,
         percent_change = `Percent Change`) %>% 
  emmeans_test(
    percent_change ~ life_stage, covariate = FTP,
    p.adjust.method = "bonferroni"
    )
pwc.sig.dif

get_emmeans(pwc.sig.dif)
```

Visuallize pairwise comparisons
```{r}
# Visualization: line plots with p-values
pwc.sig.dif <- pwc.sig.dif %>% add_xy_position(x = "life_stage", fun = "mean_se")
(slopes.sig.dif.ancova.emmeans.plot <- ggline(get_emmeans(pwc.sig.dif), x = "life_stage", y = "emmean") +
  geom_errorbar(aes(ymin = conf.low, ymax = conf.high), width = 0.2) + 
  stat_pvalue_manual(pwc.sig.dif, hide.ns = TRUE, tip.length = FALSE) +
  labs(
    subtitle = get_test_label(res.aov.sig.dif, detailed = TRUE),
    caption = get_pwc_label(pwc.sig.dif),
    x = "Life Stage", y = "Estimated Marginal Means")
)
```

stack plots
```{r}
slopes.sig.dif.ancova.stack <- ggarrange(slopes.sig.dif.linear.plot, slopes.sig.dif.ancova.emmeans.plot,
                                     ncol = 1, nrow  = 2,
                                     labels = "AUTO", legend = "right")

slopes.sig.dif.ancova.stack

ggsave(filename = "~/GitHub/EAGER_growth/Outputs/Figures/Linear Models/sig_dif_slopes_ancova_stack.tiff", 
       slopes.sig.dif.ancova.stack, 
       dpi = 300, width = 200, height = 150, units = "mm")
```

## Multi-panel plot for lm and ancova
```{r}

fig3 <- ggarrange(
  slopes.all.plot.percent.age.class,
  slopes.linear.plot, 
  slopes.all.plot.percent.juvenile,
  slopes.all.plot.percent.adult,
  ncol = 2,
  nrow = 2,
  #common.legend = T,
  legend = "right",
  labels = "AUTO",
  label.x = 0.03
)

fig3

ggsave(
  filename = "~/GitHub/EAGER_growth/Outputs/Figures/Linear Models/fig3.tiff",
  fig3,
  dpi = 300,
  width = 250,
  height = 200,
  units = "mm"
)


```

# All species 
## Model

Filter for target species from age groups that had enough data for a BRT, remove NA rows
```{r}
grow.merge.target.spp <- all.grow.merge %>% 
  filter(species %in% c("black_crappie", "bluegill", "brown_trout", "cisco", "common_white_sucker", 
                        "largemouth_bass", "northern_pike", "pumpkinseed_sunfish", "rainbow_trout", 
                        "rock_bass", "smallmouth_bass", "walleye", "yellow_perch")) %>% 
  filter(species == "black_crappie" & !age_group %in% c(12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22) |
        species == "bluegill" & !age_group %in% c(12, 13, 18) |
        species == "brown_trout" & age_group %in% c(1, 2, 3, 4, 5) |
        species == "cisco" & age_group %in% c(2, 3, 4, 5, 6, 7) |
        species == "common_white_sucker" & age_group %in% c(1, 2, 3, 4, 5) |
        species == "largemouth_bass" & !age_group %in% c(13, 14, 15, 16, 17, 18, 19, 20, 21, 22) |
        species == "northern_pike" & !age_group %in% c(11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22) |
        species == "pumpkinseed_sunfish" & age_group %in% c(1, 2, 3, 4, 5, 6, 7, 8, 9, 10) |
        species == "rainbow_trout" & age_group %in% c(1, 2, 3, 4) |
        species == "rock_bass" & age_group %in% c(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11) |
        species == "smallmouth_bass" &  age_group %in% c(0:10) |
        species == "walleye" & age_group %in% c(0:12) |
        species == "yellow_perch" & age_group %in% c(0:11)
  ) %>% 
  filter(!is.na(length_mean_mm)) %>% 
  left_join(all.slopes %>% 
              select(species, Age, FTP, `Thermal Guild`, mean_length, CTmax, `Life Stage`) %>% 
              rename(age_group = Age, life_stage = `Life Stage`),
            by = c("species", "age_group")) %>% 
  mutate(species_age_group = )
```

##

```{r}
#linear model
all.spp.lm <- lm(length_mean_mm ~ begin_date_year*age_group*species + log_max_depth + logarea + doy, 
                 data = grow.merge.target.spp)
summary(all.spp.lm)

options(na.action = "na.omit")
dd <- dredge(all.spp.lm)

#calculate and interpret effect sizes
eta_squared(all.spp.lm)
#interpret(eta_squared(all.spp.lm), rules = "cohen1992")

#calculate AIC score
AIC(all.spp.lm)

#examine model fit
testDispersion(all.spp.lm)

simulation.output <- simulateResiduals(fittedModel = all.spp.lm)

residuals(all.spp.lm)
residuals(all.spp.lm, quantileFunction = qnorm)

plot(all.spp.lm)
```

## Post-hoc comparisons
```{r}
all.spp.emm <- emmeans(all.spp.lm, ~ begin_date_year*age_group*species)
pairs(all.spp.emm, by = c("species", "age_group", "begin_date_year"))
test(pairs(all.spp.emm, by = c("species", "age_group", "begin_date_year")), by = NULL, adjust = "mvt")

#export tables
#interpret(eta_squared(all.spp.lm), rules = "cohen1992") %>%
  write.csv(file = "Outputs/Tables/all_spp_eta_squared.csv")

#slope contrasts
# Obtain slope estimates
all.spp.slopes <- emtrends(all.spp.lm, ~age_group*species, var = "begin_date_year")
# Compare estimated slopes to 0
all.spp.slope.contrasts <- test(all.spp.slopes) %>% 
  rename(Age = age_group) %>% 
  filter(!is.na(`p.value`))

all.spp.slope.contrasts %>%
  write.csv(file = "Outputs/Tables/all_spp_emmeans.csv")
```

### Plot raw data
```{r}
(all.spp.length.year.plot <- ggplot(data = grow.merge.target.spp %>% 
                              mutate(Age = as.factor(age_group)), 
                            aes(x = begin_date_year, y = length_mean_mm, 
                                color = Age, fill = Age, group = Age))+
    facet_wrap(~species, scales = "free")+
    #geom_point()+
    geom_smooth(method = "lm")+
    stat_poly_eq(use_label("eq"))+
    scale_color_viridis_d()+
    scale_fill_viridis_d()+
    labs(y = "Length (mm)", x = "Year")+
    theme_bw()
)

```
### Marginal Effects
```{r}
result <- predict_response(all.spp.lm, c("begin_date_year", "age_group", "species"))

# plot(result)
# test_predictions(result) %>% 
#   write.csv(file = "Outputs/Tables/all.spp_pairwise_length_time_slopes.csv", row.names = F)

(all.spp.marginal.plot <- ggpredict(all.spp.lm, terms = c("begin_date_year", "age_group", "species")) %>%  
    plot()+
    #stat_poly_eq(use_label("eq"))+
    stat_regline_equation()+
    stat_poly_line()+
    # annotate(geom = "text", label = "y = 77 + 0.07x", x = 2000, y = 224)+
    # annotate(geom = "text", label = "y = 21 + 0.093x", x = 2000, y = 212)+
    # annotate(geom = "text", label = "y = -64 + 0.13x", x = 2000, y = 199)+
    # annotate(geom = "text", label = "y = 230 - 0.028x", x = 2000, y = 182)+
    # annotate(geom = "text", label = "y = 610 - 0.23x", x = 2000, y = 160)+
    # annotate(geom = "text", label = "y = 920 - 0.39x", x = 2000, y = 137)+
    # annotate(geom = "text", label = "y = 1200 - 0.55x", x = 2000, y = 110)+
    # annotate(geom = "text", label = "y = 1700 - 0.83x", x = 2000, y = 80)+
    scale_color_viridis_d()+
    scale_fill_viridis_d()+
    labs(title = NULL,
         y = "Mean Length-at-Age (mm)",
         x = "Year")+
    guides(fill = guide_legend(title = "Age", reverse = T), color = guide_legend(title = "Age", reverse = T))+
    theme_bw()
)


ggsave(filename = "~/GitHub/EAGER_growth/Outputs/Figures/Linear Models/all.spp_marginal_effects_plot.tiff", 
       all.spp.marginal.plot, 
       dpi = 300, width = 200, height = 150, units = "mm")
```



# Bayesian model

Building a single model that includes all species using species as a fixed effect

Trouble Shooting 
```{r}
library(cmdstanr)
install_cmdstan()
cmdstan_path()

```

## Model setup 
```{r}
# New column that combines age_group and species
grow.merge.target.spp <- grow.merge.target.spp %>% 
  mutate(species_age_group = interaction(species, age_group))

nrow(filter(grow.merge.target.spp, Data_Type == "Contemporary"))
nrow(filter(grow.merge.target.spp, Data_Type == "Historic"))

#Main effects and two-way interactions
# formula1 <- bf(
#   length_mean_mm ~ (begin_date_year + age_group + log_max_depth + logarea + doy + species)^2
# )

#Model structure from frequentist regressions
formula2 <- bf(length_mean_mm ~ log_max_depth + logarea + doy + (1 + begin_date_year | species_age_group))

#Adding lake as a random effect
# formula3 <- bf(length_mean_mm ~ begin_date_year*age_group*species + log_max_depth + logarea + doy + (1|lakename))



```

## Run models
Fit the models
```{r}
# fit1 <- brm(
#   formula = formula1,
#   data = grow.merge.target.spp,
#   family = gaussian(),      
#   chains = 4,
#   iter = 2000,
#   cores = 4,
#   control = list(adapt_delta = 0.95)
# )

fit2 <- brm(
  formula = formula2,
  data = grow.merge.target.spp,
  family = gaussian(),      
  chains = 4,
  iter = 100000,
  thin = 10,
  cores = 4,
  control = list(adapt_delta = 0.9)
)

# fit3 <- brm(
#   formula = formula3,
#   data = grow.merge.target.spp,
#   family = gaussian(),      
#   chains = 4,
#   iter = 2000,
#   cores = 4,
#   control = list(adapt_delta = 0.95)
# )
```
## Examine results
```{r}
summary(fit2)
plot(fit2)

rhat(fit2)

write.xlsx(
  list(
    "Fixed Effects" = fixef(fit2),
    "Random Effects" = as.data.frame(ranef(fit2)),
    "Convergence" = as.data.frame(rhat(fit2))
  ),
  file = "~/GitHub/EAGER_growth/Outputs/Tables/bayesian_model_output.xlsx",
  rowNames = T
)
```

## Extract random slopes
```{r}
#Use this chunk if you've run the model and have it saved in your environment
group_effects <- ranef(fit2)$species_age_group
head(group_effects) # Shows array: [group, effect, statistic (Estimate, lower, upper)]


# Get posterior means for each group's slope
slopes <- as.data.frame(group_effects) %>% 
  #move species_age_group from the row names to a column
  rownames_to_column(var = "species_age_group") %>% 
  #rename columns to facilitate plotting
  rename(slope_bayes = "Estimate.begin_date_year",
         lower_bayes = "Q2.5.begin_date_year",
         upper_bayes = "Q97.5.begin_date_year") %>% 
  #split species_age_group into species and age_group for plotting
  separate(species_age_group, into = c("species", "age_group"), sep = "\\.") %>% 
  #fix age_group order to be numeric
  mutate(age_group = factor(age_group, levels = sort(as.numeric(unique(age_group))))) %>% 
  #change species names to remove _
  mutate(species = species %>% 
           str_replace_all("_", " ") %>% 
           str_to_title()) %>% 
  #rename age_group to make plot cleaner
  rename(Age = age_group, Species = species) %>% 
  #join with frequentist output
  left_join(all.slopes %>% 
            mutate(Species = if_else(Species == "White Sucker", "Common White Sucker", Species)), 
            by = c("Species", "Age")) %>% 
  #reorder Species by FTP for plotting
  mutate(Species = fct_reorder(Species, FTP)) %>% 
  #create percent change column for Bayes slopes
  mutate(`Bayes Percent Change` = slope_bayes/mean_length*100,
         bayes_pc_lower = lower_bayes/mean_length*100,
         bayes_pc_upper = upper_bayes/mean_length*100,
  #create column for if credibilty interval overlaps with 0
         `Statistically Different` = if_else(lower_bayes > 0 | upper_bayes < 0, "Different from 0", "Overlaps 0")
  )
```

```{r}
#Use this chunk if you don't have the model saved in your environment and are reading back in the output that was previously written out to excel
group_effects <- read.xlsx("~/GitHub/EAGER_growth/Outputs/Tables/bayesian_model_output.xlsx", 2)
head(group_effects) 


# Get posterior means for each group's slope
slopes <- as.data.frame(group_effects) %>% 
  rename_with(~str_remove(.x, "species_age_group.")) %>% 
  #rename columns to facilitate plotting
  rename(species_age_group = X1,
         slope_bayes = Estimate.begin_date_year,
         lower_bayes = Q2.5.begin_date_year,
         upper_bayes = Q97.5.begin_date_year) %>% 
  #split species_age_group into species and age_group for plotting
  separate(species_age_group, into = c("species", "age_group"), sep = "\\.") %>% 
  #fix age_group order to be numeric
  mutate(age_group = factor(age_group, levels = sort(as.numeric(unique(age_group))))) %>% 
  #change species names to remove _
  mutate(species = species %>% 
           str_replace_all("_", " ") %>% 
           str_to_title()) %>% 
  #rename age_group to make plot cleaner
  rename(Age = age_group, Species = species) %>% 
  #join with frequentist output
  left_join(all.slopes %>% 
            mutate(Species = if_else(Species == "White Sucker", "Common White Sucker", Species)), 
            by = c("Species", "Age")) %>% 
  #reorder Species by FTP for plotting
  mutate(Species = fct_reorder(Species, FTP)) %>% 
  #create percent change column for Bayes slopes
  mutate(`Bayes Percent Change` = slope_bayes/mean_length*100,
         bayes_pc_lower = lower_bayes/mean_length*100,
         bayes_pc_upper = upper_bayes/mean_length*100,
  #create column for if credibilty interval overlaps with 0
         `Statistically Different` = if_else(lower_bayes > 0 | upper_bayes < 0, "Different from 0", "Overlaps 0")
  )

```

Export output after joining with frequentist output
```{r}
write.csv(slopes, "~/GitHub/EAGER_growth/Outputs/Tables/bayes_all_slopes.csv", row.names = F)
```

Summarize by species life stage
```{r}
slopes.species.life.stage <- slopes %>% 
  group_by(Species, `Life Stage`) %>% 
  summarize(mean_slope = mean(slope_bayes), sd_slope = sd(slope_bayes),
            mean_pc = mean(`Bayes Percent Change`), sd_pc  = sd(`Bayes Percent Change`)) %>% 
  pivot_wider(names_from = `Life Stage`, 
              values_from = c(mean_slope, sd_slope, mean_pc, sd_pc)) %>% 
  select(Species, contains("Juvenile"), contains("Adult"))

slopes.age <- slopes %>% 
  group_by(Age) %>% 
  summarize(mean_slope = mean(slope_bayes), sd_slope = sd(slope_bayes),
            mean_pc = mean(`Bayes Percent Change`), sd_pc  = sd(`Bayes Percent Change`)) 
```


## Custom plots
### Percent change by age class facet by species
```{r}

thermal.guild.colors <- c("#4662D7FF", "#1AE4B6FF", "#CB2A04FF")

(fit2.species.age.groups.plot <- ggplot(slopes, aes(x = Age, y = `Bayes Percent Change`, 
                                                    color = `Thermal Guild`, fill = `Statistically Different`,
                                                    shape = `Statistically Different`)) +
  facet_wrap(~Species)+
  geom_hline(yintercept = 0, color = "red", linetype = "dashed")+
  geom_point(position=position_dodge(width=0.5), size=0.5) +
  geom_errorbar(aes(ymin=bayes_pc_lower, ymax=bayes_pc_upper),
                width=0.2, position=position_dodge(width=0.5), alpha = 0.5) +
  scale_color_manual(values = thermal.guild.colors)+
  scale_shape_manual(values = c(19, 2))+
  labs(
    y = "Annual percent change relative to mean length (mm/yr)"
  ) +
  theme_bw()+
  theme(strip.text = element_text(size = 8))
)

ggsave(
  ggsave(filename = "~/GitHub/EAGER_growth/Outputs/Figures/Linear Models/bayes_all_slopes_percent_age_class_spp_facet.tiff", 
       fit2.species.age.groups.plot, 
       dpi = 300, width = 200, height = 150, units = "mm")
)
```

Species on x-axis with age labels
```{r}
bayes.all.slopes.pc.CI <- slopes %>%
  group_by(Species) %>%
  summarize(
    mean = mean(`Percent Change`),
    sd = sd(`Percent Change`),
    n = n(),
    se = sd / sqrt(n),
    ci_lower = mean - qt(0.975, n - 1) * se,
    ci_upper = mean + qt(0.975, n - 1) * se
  )

(
  bayes.slopes.all.plot.percent <- ggplot() +
    geom_hline(
      yintercept = 0,
      color = "red",
      linetype = "dashed"
    ) +
    geom_errorbar(data = bayes.all.slopes.pc.CI,
                  aes(x = Species, ymin = ci_lower, ymax = ci_upper),
                  width = 0.2) +
    geom_point(data = all.slopes.pc.CI, aes(x = Species, y = mean), size = 3) +
    geom_point(
      data = slopes ,
      aes(
        x = Species,
        y = `Percent Change`,
        color = `Thermal Guild`,
        fill = `Thermal Guild`,
        shape = `Statistically Different`,
        group = Species
      ),
      size = 2
    ) +
    scale_color_manual(values = thermal.guild.colors) +
    scale_fill_manual(values = thermal.guild.colors) +
    scale_shape_manual(values = c(2, 16)) +
    geom_text_repel(data = slopes,
      aes(x = Species, y = `Percent Change`, label = Age),
      show.legend = F,
      max.overlaps = 100,
      max.iter = 100000
    ) +
    labs(y = "Annual Percent Change in Length-at-Age", x = NULL) +
    theme_bw()+
    theme(
      #panel.background = element_rect(fill = "gray"),
      ,
      axis.text.x = element_text(
        angle = 45,
        hjust = 1,
        size = 12
      ),
      axis.text.y = element_text(size = 10),
      axis.title.x = element_text(size = 12)
    )
)

ggsave(filename = "~/GitHub/EAGER_growth/Outputs/Figures/Linear Models/bayes_slopes_percent_thermal.tiff", 
       bayes.slopes.all.plot.percent, 
       dpi = 300, width = 200, height = 150, units = "mm")
```


### Plot by species no facet
```{r}
bayes.all.slopes.CI <- slopes %>%
  group_by(Species) %>%
  summarize(
    mean = mean(slope_bayes),
    sd = sd(slope_bayes),
    n = n(),
    se = sd / sqrt(n),
    ci_lower = mean - qt(0.975, n - 1) * se,
    ci_upper = mean + qt(0.975, n - 1) * se
  )

(
  bayes.slopes.all.plot <- ggplot() +
    geom_hline(
      yintercept = 0,
      color = "red",
      linetype = "dashed"
    ) +
    geom_errorbar(
      data = bayes.all.slopes.CI,
      aes(x = Species, ymin = ci_lower, ymax = ci_upper),
      width = 0.2
    ) +
    geom_point(data = bayes.all.slopes.CI, aes(x = Species, y = mean), size = 3) +
    geom_jitter(
      data = slopes,
      aes(
        x = Species,
        y = Slope,
        color = `Thermal Guild`,
        fill = `Thermal Guild`,
        shape = `Statistically Different`,
        group = Species
      ),
      size = 2,
      width = 0.2
    ) +
    scale_color_manual(values = thermal.guild.colors) +
    scale_fill_manual(values = thermal.guild.colors) +
    scale_shape_manual(values = c(19, 2)) +
    labs(y = "Change in Length-at-Age (mm/yr)", x = NULL) +
    theme_bw()+
    theme(
      #panel.background = element_rect(fill = "gray"),
      axis.text.x = element_text(angle = 45, hjust = 1, size = 12),
      axis.text.y = element_text(size = 10),
      axis.title.x = element_text(size = 12)
    )
)

ggsave(filename = "~/GitHub/EAGER_growth/Outputs/Figures/Linear Models/bayes_all_slopes_species.tiff", 
       bayes.slopes.all.plot, 
       dpi = 300, width = 200, height = 150, units = "mm")
```
### Plot by age no facet
```{r}
bayes.all.slopes.age.CI <- slopes %>%
  group_by(Age) %>%
  summarize(
    mean = mean(`Bayes Percent Change`),
    sd = sd(`Bayes Percent Change`),
    n = n(),
    se = sd / sqrt(n),
    ci_lower = mean - qt(0.975, n - 1) * se,
    ci_upper = mean + qt(0.975, n - 1) * se
  )

(
  bayes.slopes.all.age.percent.change.plot <- ggplot() +
    geom_hline(
      yintercept = 0,
      color = "red",
      linetype = "dashed"
    ) +
    geom_errorbar(
      data = bayes.all.slopes.age.CI,
      aes(x = Age, ymin = ci_lower, ymax = ci_upper),
      width = 0.2
    ) +
    geom_point(data = bayes.all.slopes.age.CI, aes(x = Age, y = mean), size = 3) +
    geom_jitter(
      data = slopes,
      aes(
        x = Age,
        y = `Bayes Percent Change`,
        color = `Thermal Guild`,
        fill = `Thermal Guild`,
        shape = `Statistically Different`,
        group = Species
      ),
      size = 2,
      width = 0.2
    ) +
    scale_color_manual(values = thermal.guild.colors) +
    scale_fill_manual(values = thermal.guild.colors) +
    scale_shape_manual(values = c(19, 2)) +
    labs(y = "Percent Change", x = NULL) +
    theme_bw()+
    theme(
      #panel.background = element_rect(fill = "gray"),
      #axis.text.x = element_text(angle = 45, hjust = 1, size = 12),
      axis.text.y = element_text(size = 10),
      axis.title.x = element_text(size = 12)
    )
)

ggsave(filename = "~/GitHub/EAGER_growth/Outputs/Figures/Linear Models/bayes_all_slopes_age_percent_change.tiff", 
       bayes.slopes.all.age.percent.change.plot, 
       dpi = 300, width = 200, height = 150, units = "mm")
```

### Break adults and juveniles into their own plots
Juveniles
```{r}
bayes.all.slopes.life.stage.CI <- slopes %>%
  #rename(`Life Stage` = life_stage) %>% 
  group_by(Species, `Life Stage`) %>%
  summarize(
    mean = mean(`Bayes Percent Change`),
    sd = sd(`Bayes Percent Change`),
    n = n(),
    se = sd / sqrt(n),
    ci_lower = mean - qt(0.975, n - 1) * se,
    ci_upper = mean + qt(0.975, n - 1) * se
  )%>% 
  mutate(`Life Stage` = fct_rev(`Life Stage`))


(
  bayes.slopes.all.plot.percent.juvenile <- ggplot(data = bayes.all.slopes.life.stage.CI %>% 
                                                     filter(`Life Stage` == "Juvenile")) +
    geom_hline(
      yintercept = 0,
      color = "red",
      linetype = "dashed"
    ) +
    geom_errorbar(
      aes(
        x = Species,
        ymin = ci_lower,
        ymax = ci_upper,
        group = interaction(`Life Stage`, Species)
      ),
      position = position_dodge(width = 0.4),
      width = 0.2
    ) +
    geom_point(
               aes(
                 x = Species,
                 y = mean,
                 group = interaction(`Life Stage`, Species)
               ),
               position = position_dodge(width = 0.4),
               size = 3) +
    geom_jitter(
      data = slopes %>%
        filter(`Life Stage` == "Juvenile"),
      aes(
        x = Species,
        y = `Bayes Percent Change`,
        color = `Thermal Guild`,
        fill = `Thermal Guild`,
        shape = `Statistically Different`,
        #group = interaction(`Life Stage`, Species)
      ),
      position = position_dodge(width = 0.4),
      size = 2
    ) +
    scale_color_manual(values = thermal.guild.colors) +
    scale_fill_manual(values = thermal.guild.colors) +
    scale_shape_manual(values = c(19, 2)) +
    labs(y = "Percent Change", 
         x = NULL,
         title = "Juveniles") +
    theme_bw()+
    theme(
      #panel.background = element_rect(fill = "gray"),
      ,
      axis.text.x = element_text(
        angle = 45,
        hjust = 1,
        size = 12
      ),
      axis.text.y = element_text(size = 10),
      axis.title.x = element_text(size = 12)
    )
)
```
Adults
```{r}
(
  bayes.slopes.all.plot.percent.adult <- ggplot(data = bayes.all.slopes.life.stage.CI %>% 
                                                 filter(`Life Stage` == "Adult")) +
    geom_hline(
      yintercept = 0,
      color = "red",
      linetype = "dashed"
    ) +
    geom_errorbar(
      aes(
        x = Species,
        ymin = ci_lower,
        ymax = ci_upper,
        group = interaction(`Life Stage`, Species)
      ),
      position = position_dodge(width = 0.4),
      width = 0.2
    ) +
    geom_point(
               aes(
                 x = Species,
                 y = mean,
                 group = interaction(`Life Stage`, Species)
               ),
               position = position_dodge(width = 0.4),
               size = 3) +
    geom_jitter(
      data = slopes %>%
        filter(`Life Stage` == "Adult"),
      aes(
        x = Species,
        y = `Bayes Percent Change`,
        color = `Thermal Guild`,
        fill = `Thermal Guild`,
        shape = `Statistically Different`,
        #group = interaction(`Life Stage`, Species)
      ),
      position = position_dodge(width = 0.4),
      size = 2
    ) +
    scale_color_manual(values = thermal.guild.colors) +
    scale_fill_manual(values = thermal.guild.colors) +
    scale_shape_manual(values = c(19, 2)) +
    labs(y = "Percent Change", 
         x = NULL,
         title = "Adults") +
    theme_bw()+
    theme(
      #panel.background = element_rect(fill = "gray"),
      ,
      axis.text.x = element_text(
        angle = 45,
        hjust = 1,
        size = 12
      ),
      axis.text.y = element_text(size = 10),
      axis.title.x = element_text(size = 12)
    )
)

```

## Model slopes w/ FTP and Life Stage

###Visualize Relationships
```{r}
(
  bayes.slopes.linear.plot <- ggscatter(
    data = slopes,
    x = "FTP",
    y = "Bayes Percent Change",
    color = "Life Stage",
    add = "reg.line",
    alpha = 0.5
  ) +
    stat_regline_equation(aes(
      label =  paste(..eq.label.., ..rr.label.., sep = "~~~~"),
      color = `Life Stage`
    ), size = 2.5, label.y.npc = "bottom") +
    scale_color_viridis_d(end = 0.9, option = "C") +
    labs(y = "Percent Change") +
    theme(text = element_text(size = 12))
)

ggsave(
  filename = "~/GitHub/EAGER_growth/Outputs/Figures/Linear Models/bayes_ftp_life_stage_slopes_raw_data.tiff",
  bayes.slopes.linear.plot,
  width = 200,
  height = 150,
  units = "mm",
  dpi = 300
)

(
  bayes.slopes.linear.plot.remove.spp <- ggscatter(
    data = slopes %>%
      filter(!Species %in% c("Rainbow Trout", "Northern Pike")),
    x = "FTP",
    y = "Bayes Percent Change",
    color = "Life Stage",
    add = "reg.line",
    alpha = 0.5
  ) +
    stat_regline_equation(aes(
      label =  paste(..eq.label.., ..rr.label.., sep = "~~~~"),
      color = `Life Stage`
    ), size = 2.5, label.y.npc = "bottom") +
    scale_color_viridis_d(end = 0.9, option = "C") +
    labs(y = "Percent Change")+
    theme(text = element_text(size = 12))
)

ggsave(
  filename = "~/GitHub/EAGER_growth/Outputs/Figures/Linear Models/bayes_ftp_life_stage_slopes_raw_data_spp_removed.tiff",
  bayes.slopes.linear.plot.remove.spp,
  width = 200,
  height = 150,
  units = "mm",
  dpi = 300
)


(bayes.slopes.boxplot <- ggplot(slopes, aes(x = `Life Stage`, y = slope_bayes))+
    geom_point(aes(color = Species))+
    geom_boxplot(outliers = F)
)

(bayes.pc.boxplot <- ggplot(slopes, aes(x = `Life Stage`, y = `Bayes Percent Change`))+
  geom_point(aes(color = Species))+
  geom_boxplot(outliers = F)
)


```

### Model
Meta-analytic model
```{r}
slopes <- slopes %>% 
  rename(se_slope = Est.Error.begin_date_year,
         life_stage = `Life Stage`)

second_model_bayes <- brm( formula = bf(
  slope_bayes | se(se_slope) ~ FTP*life_stage),
  data = slopes,
  family = gaussian(),
  chains = 4,
  cores = 4,
  control = list(adapt_delta = 0.8)
)

summary(second_model_bayes)
```

Post-hoc comparisons
```{r}
# 'emtrends' estimates the slope of FTP within each life_stage level
ftp_slopes <- emtrends(second_model_bayes, ~ life_stage, var = "FTP")
summary(ftp_slopes)
pairs(ftp_slopes)  # Compare FTP slope between life stages
```

Extract posterior summary
```{r}
#create range of FTP to predict over
pred_grid <- expand.grid(
  FTP = seq(min(slopes$FTP, na.rm = TRUE), max(slopes$FTP, na.rm = TRUE), length.out = 100),
  life_stage = unique(slopes$life_stage),
  se_slope = 0
)

#extract posterior summaries and intervals for each combination of FTP and Life Stage
predictions <- add_fitted_draws(
  model = second_model_bayes,
  newdata = pred_grid,
  re_formula = NA  # Ensures population-level prediction
)

#summarize
pred_summary <- predictions %>%
  group_by(FTP, life_stage) %>%
  summarise(
    median = median(.value),
    lower = quantile(.value, 0.025),
    upper = quantile(.value, 0.975)
  ) %>%
  ungroup()
```

Plot
```{r}
(
  partial.effects.life.stage.ftp.plot <- ggplot(
    pred_summary %>% 
      mutate(life_stage = fct_relevel(life_stage, "Adult", "Juvenile")),
    aes(
      x = FTP,
      y = median,
      color = life_stage,
      fill = life_stage
    )
  ) +
    geom_ribbon(
      aes(ymin = lower, ymax = upper),
      alpha = 0.2,
      color = NA
    ) +
    geom_line(size = 1) +
    labs(
      y = "Change in total length (mm) per year",
      x = "FTP",
      fill = "Life Stage",
      color = "Life Stage"
    ) +
    scale_fill_viridis_d(end = 0.9, option = "C") +
    scale_color_viridis_d(end = 0.9, option = "C") +
    theme_bw()
)
```

### Remove Rainbow Trout and Northern Pike
```{r}
second_model_bayes_spp_removed <- brm( formula = bf(
  slope_bayes | se(se_slope) ~ FTP*life_stage),
  data = slopes %>% filter(Species %in% c("Rainbow Trout", "Northern Pike")),
  family = gaussian(),
  chains = 4,
  cores = 4,
  control = list(adapt_delta = 0.8)
)

summary(second_model_bayes_spp_removed)
```

Post-hoc comparisons
```{r}
# 'emtrends' estimates the slope of FTP within each life_stage level
ftp_slopes_spp_removed <- emtrends(second_model_bayes_spp_removed, ~ life_stage, var = "FTP")
summary(ftp_slopes_spp_removed)
pairs(ftp_slopes_spp_removed)  # Compare FTP slope between life stages
```

Extract posterior summary
```{r}
#create range of FTP to predict over
pred_grid_spp_removed <- expand.grid(
  FTP = seq(min(slopes$FTP, na.rm = TRUE), max(slopes$FTP, na.rm = TRUE), length.out = 100),
  life_stage = unique(slopes$life_stage),
  se_slope = 0
)

#extract posterior summaries and intervals for each combination of FTP and Life Stage
predictions_spp_removed <- add_fitted_draws(
  model = second_model_bayes_spp_removed,
  newdata = pred_grid_spp_removed,
  re_formula = NA  # Ensures population-level prediction
)

#summarize
pred_summary_spp_removed <- predictions_spp_removed %>%
  group_by(FTP, life_stage) %>%
  summarise(
    median = median(.value),
    lower = quantile(.value, 0.025),
    upper = quantile(.value, 0.975)
  ) %>%
  ungroup()
```

Plot
```{r}
(
  partial.effects.life.stage.ftp.plot.spp.removed <- ggplot(
    pred_summary_spp_removed %>%
      mutate(life_stage = fct_relevel(life_stage, "Adult", "Juvenile")),
    aes(
      x = FTP,
      y = median,
      color = life_stage,
      fill = life_stage
    )
  ) +
    geom_ribbon(
      aes(ymin = lower, ymax = upper),
      alpha = 0.2,
      color = NA
    ) +
    geom_line(size = 1) +
    labs(
      y = "Change in total length (mm) per year",
      x = "FTP",
      fill = "Life Stage",
      color = "Life Stage"
    ) +
    scale_fill_viridis_d(
      end = 0.9,
      option = "C"
    ) +
    scale_color_viridis_d(
      end = 0.9,
      option = "C"
    ) +
    theme_bw()
)
```

Fully Bayesian two-level/joint model via posterior simulation - did not end up using this approach
<!-- # ```{r} -->
<!-- # #extract posterior draws -->
<!-- # draws <- fit2 %>% -->
<!-- #   spread_draws( -->
<!-- #     r_species_age_group[species_age_group, begin_date_year] -->
<!-- #   ) %>% -->
<!-- #   mutate( -->
<!-- #     slope = r_species_age_group -->
<!-- #   ) %>%  -->
<!-- #   #format to join -->
<!-- #   separate(species_age_group, into = c("Species", "Age"), sep = "[//.]") %>%  -->
<!-- #   mutate(Species = Species %>%  -->
<!-- #            str_replace_all("_", " ") %>%  -->
<!-- #            str_to_title()) %>%  -->
<!-- #   #join with other covariates to set up model -->
<!-- #   left_join(slopes %>%  -->
<!-- #               select(Species, Age, FTP, life_stage), -->
<!-- #             by = c("Species", "Age")) -->
<!-- #  -->
<!-- # #nest posterior draws -->
<!-- # draws_nested <- draws %>% -->
<!-- #   select(.draw, Species, Age, slope, FTP, life_stage) %>% -->
<!-- #   group_by(.draw) %>% -->
<!-- #   nest() -->
<!-- #  -->
<!-- # #function to fit a linear model slope ~ FTP + Life Stage for each posterior draw -->
<!-- # fit_lm_for_draw <- function(df) { -->
<!-- #   summary(lm(slope ~ FTP*life_stage, data = df))$coef[, "Estimate"] -->
<!-- # } -->
<!-- #  -->
<!-- # #apply the function to each nested data set -->
<!-- # draws_nested <- draws_nested %>% -->
<!-- #   mutate(beta = map(data, fit_lm_for_draw)) -->
<!-- #  -->
<!-- # #unnest posterior samples -->
<!-- # posterior_coefs <- draws_nested %>% -->
<!-- #   select(.draw, beta) %>% -->
<!-- #   unnest_wider(beta)  # One column per coefficient -->
<!-- #  -->
<!-- # posthoc_draws <- posterior_coefs %>% -->
<!-- #   rename( -->
<!-- #     FTP_main = FTP, -->
<!-- #     FTP_Adult = `FTP:life_stageJuvenile`          -->
<!-- #   )%>% -->
<!-- #   transmute( -->
<!-- #     ftp_slope_adult = FTP_main, -->
<!-- #     ftp_slope_juvenile = FTP_main + FTP_Adult, -->
<!-- #     ftp_slope_diff = FTP_Adult    # (Adult - Juvenile), same as interaction term -->
<!-- # ) -->
<!-- #  -->
<!-- # posterior_coefs_summary <- posthoc_draws %>%   -->
<!-- #   summarise( -->
<!-- #     juvenile_slope_mean = mean(ftp_slope_juvenile), -->
<!-- #     juvenile_slope_l95 = quantile(ftp_slope_juvenile, 0.025), -->
<!-- #     juvenile_slope_u95 = quantile(ftp_slope_juvenile, 0.975), -->
<!-- #     adult_slope_mean = mean(ftp_slope_adult), -->
<!-- #     adult_slope_l95 = quantile(ftp_slope_adult, 0.025), -->
<!-- #     adult_slope_u95 = quantile(ftp_slope_adult, 0.975), -->
<!-- #     diff_mean = mean(ftp_slope_diff), -->
<!-- #     diff_l95 = quantile(ftp_slope_diff, 0.025), -->
<!-- #     diff_u95 = quantile(ftp_slope_diff, 0.975) -->
<!-- #   ) -->
<!-- # ``` -->
<!-- #  -->
<!-- # Visualize -->
<!-- # ```{r} -->
<!-- # posthoc_long <- posthoc_draws %>% -->
<!-- #   pivot_longer(cols = starts_with("ftp_slope_"), names_to = "type", values_to = "value") -->
<!-- #  -->
<!-- # (ftp.posterior.slopes <- ggplot(posthoc_long %>%  -->
<!-- #                                   filter(type %in% c("ftp_slope_juvenile", "ftp_slope_adult")) %>%  -->
<!-- #                                   mutate(type = if_else(type == "ftp_slope_juvenile", "Adult","Juvenile")), -->
<!-- #        aes(x = value, fill = type)) + -->
<!-- #   geom_density(alpha = 0.8) + -->
<!-- #   labs(x = "FTP Slope", y = "Posterior Density",  -->
<!-- #        #title = "Posterior FTP Slopes by Life Stage", -->
<!-- #        fill = "Group") + -->
<!-- #   #scale_fill_manual(labels = c("Adult", "Juvenile"), values = c("#1b9e77", "#d95f02")) -->
<!-- #   scale_fill_viridis_d(end = 0.8, option = "C")+ -->
<!-- #   scale_y_continuous(expand = c(0,0))+ -->
<!-- #   theme_bw() -->
<!-- # ) -->
<!-- # ``` -->
<!-- #  -->
<!-- # ```{r} -->
<!-- # ggplot(posthoc_draws, aes(x = ftp_slope_diff)) + -->
<!-- #   geom_density(fill = "#7570b3", alpha = 0.5) + -->
<!-- #   geom_vline(aes(xintercept = 0), linetype = "dashed", color = "red") + -->
<!-- #   labs( -->
<!-- #     x = "Difference in FTP Slope (Adult - Juvenile)", -->
<!-- #     y = "Posterior Density", -->
<!-- #     title = "Posterior Difference in FTP Slope: Adult vs Juvenile" -->
<!-- #   ) -->
<!-- #  -->
<!-- # ci <- quantile(posthoc_draws$ftp_slope_diff, c(0.025, 0.975)) -->
<!-- # ggplot(posthoc_draws, aes(x = ftp_slope_diff)) + -->
<!-- #   geom_density(fill = "#7570b3", alpha = 0.5) + -->
<!-- #   geom_vline(xintercept = 0, linetype = "dashed") + -->
<!-- #   geom_vline(xintercept = ci, linetype = "dotted", color = "darkgrey") + -->
<!-- #   annotate("text", x = ci[1], y = 0, label = sprintf("%.2f", ci[1]), vjust = -1.5) + -->
<!-- #   annotate("text", x = ci[2], y = 0, label = sprintf("%.2f", ci[2]), vjust = -1.5) + -->
<!-- #   labs(x = "Difference in FTP Slope (Adult - Juvenile)", y = "Posterior Density") -->
<!-- # ``` -->

### Multipanel figures
#### Figure 3
```{r}
fig3.bayes <- ggarrange(
  bayes.slopes.all.age.percent.change.plot,
  partial.effects.life.stage.ftp.plot, 
  bayes.slopes.all.plot.percent.juvenile,
  bayes.slopes.all.plot.percent.adult,
  ncol = 2,
  nrow = 3,
  #common.legend = T,
  legend = "right",
  labels = "AUTO"
)

fig3.bayes

ggsave(
  filename = "~/GitHub/EAGER_growth/Outputs/Figures/Linear Models/fig3_bayes.tiff",
  fig3.bayes,
  dpi = 300,
  width = 300,
  height = 300,
  units = "mm"
)
```
#### Figure S14
```{r}
figS14 <- ggarrange(
  bayes.slopes.linear.plot,
  bayes.slopes.linear.plot.remove.spp,
  partial.effects.life.stage.ftp.plot,
  partial.effects.life.stage.ftp.plot.spp.removed,
  ncol = 2,
  nrow = 2,
  common.legend = T,
  legend = "right",
  labels = "AUTO",
  hjust = -1.4
)

figS14

ggsave(
  filename = "~/GitHub/EAGER_growth/Outputs/Figures/Linear Models/figS14.tiff",
  figS14,
  dpi = 300,
  width = 200,
  height = 150,
  units = "mm"
)
```

# Post-2015
Aging protocols changed after 2015 to include multiple structures for some species.
We are investigating if there is any evidence that trends change post-2015.

## Plot raw data
```{r}
grow.merge.target.spp.clean <- grow.merge.target.spp %>% 
  mutate(species = species %>% 
           str_replace_all("_", " ") %>% 
           str_to_title()) %>% 
  rename(`Age Group` = age_group)

(all.spp.year <- ggplot(data = grow.merge.target.spp.clean, 
                        aes(x = begin_date_year, y = length_mean_mm, 
                            color = `Age Group`, fill = `Age Group`))+
   #geom_point(alpha = 0.1)+
   geom_smooth()+
   geom_vline(xintercept = 2015, color = "red", linetype = "dashed")+
   facet_wrap(~species, scales = "free")+
   scale_y_continuous(expand = c(0,0))+
   scale_color_viridis_d()+
   scale_fill_viridis_d()+
   theme_bw()
)

#No obvious differences in trends post-2015

ggsave(
  filename = "~/GitHub/EAGER_growth/Outputs/Figures/raw_data_smooth_spp_age_group.tiff",
  all.spp.year,
  dpi = 300,
  width = 300,
  height = 250,
  units = "mm"
)
```


